diff --git a/.ai-instructions b/.ai-instructions
new file mode 160000
index 00000000..f2744aab
--- /dev/null
+++ b/.ai-instructions
@@ -0,0 +1 @@
+Subproject commit f2744aabcc974fd101240c2e4393de232f45b061
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index b5012ed2..ca658f18 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -23,27 +23,42 @@ jobs:
           - macos-latest
           - windows-latest
         python-version:
-          - '3.13'
+          - '3.14'
     steps:
       - uses: actions/checkout@v6
-      - uses: prefix-dev/setup-pixi@v0.9.3
+      - uses: prefix-dev/setup-pixi@v0.9.4
         with:
-          pixi-version: v0.62.2
+          pixi-version: v0.66.0
           cache: true
           cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
-          environments: test-cpu
+          environments: tests-cpu
           activate-environment: true
           frozen: true
       - name: Run pytest
-        if: runner.os != 'Linux' || matrix.python-version != '3.13'
+        if: runner.os != 'Linux' || matrix.python-version != '3.14'
         shell: bash -l {0}
-        run: pixi run -e test-cpu tests
+        run: pixi run -e tests-cpu tests
       - name: Run pytest with coverage
-        if: runner.os == 'Linux' && matrix.python-version == '3.13'
+        if: runner.os == 'Linux' && matrix.python-version == '3.14'
         shell: bash -l {0}
-        run: pixi run -e test-cpu tests-with-cov
+        run: pixi run -e tests-cpu tests-with-cov
       - name: Upload coverage report
-        if: runner.os == 'Linux' && matrix.python-version == '3.13'
+        if: runner.os == 'Linux' && matrix.python-version == '3.14'
         uses: codecov/codecov-action@v5
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+  run-ty:
+    name: Run ty
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v6
+      - uses: prefix-dev/setup-pixi@v0.9.4
+        with:
+          pixi-version: v0.66.0
+          cache: true
+          cache-write: ${{ github.event_name == 'push' && github.ref_name == 'main' }}
+          frozen: true
+          environments: type-checking
+      - name: Run ty
+        run: pixi run -e type-checking ty
+        shell: bash -el {0}
diff --git a/.gitignore b/.gitignore
index edcf29f8..9ed8d4cc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+# Claude Code
+.claude/
+
 # Distribution / packaging
 *.egg
 *.egg-info/
@@ -13,7 +16,6 @@ wheels/
 
 # Documentation
 docs/_build/
-_build/
 
 # IDE
 .idea/
@@ -27,6 +29,7 @@ _build/
 
 # pixi
 .pixi/
+node_modules/
 
 # Python
 __pycache__/
@@ -48,5 +51,3 @@ htmlcov/
 
 # Version file (generated by hatch-vcs)
 src/*/_version.py
-
-.claude
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 00000000..10e09e46
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,4 @@
+[submodule ".ai-instructions"]
+	path = .ai-instructions
+	url = git@github.com:OpenSourceEconomics/ai-instructions.git
+	branch = make-submodule
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 300eaf14..2877891b 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,10 +4,10 @@ repos:
     hooks:
       - id: check-hooks-apply
       - id: check-useless-excludes
-  - repo: https://github.com/tox-dev/tox-toml-fmt
-    rev: v1.2.2
+  - repo: https://github.com/tox-dev/pyproject-fmt
+    rev: v2.19.0
     hooks:
-      - id: tox-toml-fmt
+      - id: pyproject-fmt
   - repo: https://github.com/lyz-code/yamlfix
     rev: 1.19.1
     hooks:
@@ -17,7 +17,7 @@ repos:
     hooks:
       - id: check-added-large-files
         args:
-          - --maxkb=50000
+          - --maxkb=10000
       - id: check-ast
       - id: check-case-conflict
       - id: check-docstring-first
@@ -47,7 +47,7 @@ repos:
     hooks:
       - id: yamllint
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.14
+    rev: v0.15.6
     hooks:
       - id: ruff-check
         args:
@@ -62,7 +62,7 @@ repos:
           - pyi
           - python
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.9.0
+    rev: 0.9.1
     hooks:
       - id: nbstripout
         args:
@@ -79,6 +79,6 @@ repos:
         args:
           - --wrap
           - '88'
-        files: (CLAUDE\.md|README\.md)
+        files: (AGENTS\.md|CLAUDE\.md|README\.md|modules/.*\.md|profiles/.*\.md)
 ci:
   autoupdate_schedule: monthly
diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index f0038d2b..d7753359 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -16,5 +16,7 @@ build:
       # Jupyter Book 2.0 builds site content to _build/html.
       # For ReadTheDocs, we build and then copy to the expected output location.
       - mkdir --parents $READTHEDOCS_OUTPUT/html/
-      - BASE_URL="/$READTHEDOCS_LANGUAGE/$READTHEDOCS_VERSION" pixi run -e docs docs
+      - >-
+        BASE_URL="/$READTHEDOCS_LANGUAGE/$READTHEDOCS_VERSION"
+        pixi run -e docs build-docs
       - cp -a docs/_build/html/. "$READTHEDOCS_OUTPUT/html" && rm -r docs/_build
diff --git a/.yamllint.yml b/.yamllint.yml
index 631965cd..5c9fd42a 100644
--- a/.yamllint.yml
+++ b/.yamllint.yml
@@ -29,11 +29,8 @@ rules:
   quoted-strings: disable
   trailing-spaces: enable
   truthy:
-    check-keys: false
     level: warning
 yaml-files:
   - '*.yaml'
   - '*.yml'
   - .yamllint
-ignore:
-  - src/skillmodels/test_data/simplest_augmented_model.yaml
diff --git a/CLAUDE.md b/CLAUDE.md
index c6a1547c..ed53c3ff 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -1,3 +1,6 @@
+@.ai-instructions/profiles/tier-a.md @.ai-instructions/modules/jax.md
+@.ai-instructions/modules/optimagic.md
+
 # CLAUDE.md
 
 This file provides guidance to Claude Code (claude.ai/code) when working with code in
@@ -9,20 +12,23 @@ skillmodels is a Python implementation of estimators for nonlinear dynamic laten
 models, primarily used for skill formation research in economics. It implements Kalman
 filter-based maximum likelihood estimation following Cunha, Heckman, Schennach (2010).
 
+Used as the core estimation engine by sibling application projects (`skane-struct-bw`,
+`health-cognition`) in the parent workspace.
+
 ## Development Commands
 
 ```bash
 # Run tests
-pixi run tests
+pixi run -e tests-cpu tests
 
 # Run tests with coverage
-pixi run tests-with-cov
+pixi run -e tests-cpu tests-with-cov
 
 # Run a single test file
-pixi run -e test-cpu pytest tests/test_kalman_filters.py
+pixi run -e tests-cpu pytest tests/test_kalman_filters.py
 
 # Run a single test
-pixi run -e test-cpu pytest tests/test_kalman_filters.py::test_function_name
+pixi run -e tests-cpu pytest tests/test_kalman_filters.py::test_function_name
 
 # Type checking
 pixi run ty
@@ -30,8 +36,8 @@ pixi run ty
 # Quality checks (linting, formatting)
 prek run --all-files
 
-# Build documentation (from docs/ directory)
-make html
+# Build documentation (mystmd, from docs/ directory)
+myst build
 ```
 
 ## Command Rules
@@ -40,14 +46,14 @@ Always use these command mappings:
 
 - **Python**: Use `pixi run python` instead of `python` or `python3`
 - **Type checker**: Use `pixi run ty` instead of running ty/mypy/pyright directly
-- **Tests**: Use `pixi run tests` instead of `pytest` directly
+- **Tests**: Use `pixi run -e tests-cpu tests` instead of `pytest` directly
 - **Linting/formatting**: Use `prek run --all-files` instead of `ruff` directly
 - **All quality checks**: Use `prek run --all-files`
 
 Before finishing any task that modifies code, always run:
 
 1. `pixi run ty` (type checker)
-1. `pixi run tests` (tests)
+1. `pixi run -e tests-cpu tests` (tests)
 1. `prek run --all-files` (quality checks)
 
 ## Architecture
@@ -55,32 +61,91 @@ Before finishing any task that modifies code, always run:
 ### Core Pipeline Flow
 
 ```
-Model Dict + Data
-       ↓
-process_model() → Validates/extends model specification
+ModelSpec + Data
        ↓
-process_data() → Transforms data to estimation format
+process_model() → Validates/extends model specification → ProcessedModel
        ↓
-get_maximization_inputs() → Creates optimization problem (likelihood, gradients, constraints)
+get_maximization_inputs() → Creates optimization problem (likelihood, gradients,
+                            constraints, params_template)
        ↓
-[optimagic optimization]
+[optimagic maximize / estimagic estimate_ml with fides algorithm]
        ↓
 get_filtered_states() → Extract estimated latent factors
+simulate_dataset() → Simulate states (with optional policy effects)
 ```
 
 ### Key Modules
 
-- **process_model.py**: Model specification validation and preprocessing. Handles
-  dimensions, labels, stagemap, anchoring, and endogenous factors.
+- **model_spec.py**: User-facing frozen dataclasses (`ModelSpec`, `FactorSpec`,
+  `AnchoringSpec`). Re-exports `EstimationOptions` and `Normalizations` from `types.py`.
+  `ModelSpec` supports construction via `__init__` or `ModelSpec.from_dict()`, and
+  fluent builder methods: `with_transition_functions()`, `with_added_factor()`,
+  `with_added_observed_factors()`, `with_estimation_options()`, `with_anchoring()`,
+  `with_controls()`, `with_stagemap()`.
+- **types.py**: Internal frozen dataclasses (`ProcessedModel`, `Labels`, `Dimensions`,
+  `Anchoring`, `ParsingInfo`, `ParsedParams`, `EndogenousFactorsInfo`, etc.),
+  `EstimationOptions`, `Normalizations`, and immutability utilities.
+- **process_model.py**: Model specification validation and preprocessing. Converts
+  `ModelSpec` into `ProcessedModel`.
 - **kalman_filters.py**: Core Kalman filter implementation (predict/update steps). Uses
   square-root form for numerical stability.
-- **likelihood_function.py**: Log-likelihood computation using Kalman filtering.
-  Includes soft clipping for numerical stability.
+- **likelihood_function.py** / **likelihood_function_debug.py**: Log-likelihood
+  computation using Kalman filtering. The debug variant is not jitted and returns
+  intermediate results (residuals, contributions, filtered states).
 - **constraints.py**: Generates parameter constraints (bounds, equalities from stagemap,
-  fixed values) for optimization.
+  fixed values) for optimization. Exports `get_constraints_dicts()`,
+  `constraints_dicts_to_om()`, `enforce_fixed_constraints()`, `add_bounds()`.
 - **parse_params.py**: Converts flat parameter vectors to structured model parameters.
-- **transition_functions.py**: Pre-built transition equations (`linear`, `log_ces`,
-  `constant`). Custom functions can be added.
+  Exports `create_parsing_info()` and `parse_params()`.
+- **params_index.py**: Builds the `pd.MultiIndex` for the params DataFrame via
+  `get_params_index()`.
+- **transition_functions.py**: Pre-built transition equations: `linear`, `translog`,
+  `robust_translog`, `linear_and_squares`, `log_ces`, `log_ces_general`, `constant`.
+- **decorators.py**: `register_params` decorator for custom transition functions. Tags a
+  callable with `__registered_params__` so skillmodels knows its parameter names.
+- **process_data.py**: `process_data()` for internal estimation format,
+  `pre_process_data()` for reshaping data to long format with period indexing.
+- **simulate_data.py**: `simulate_dataset()` and `simulate_policy_effect()`.
+- **diagnostic_plots.py**: `plot_likelihood_contributions()` and
+  `plot_residual_boxplots()` (Plotly-based).
+- **variance_decomposition.py**: `decompose_measurement_variance()` and
+  `summarize_measurement_reliability()`.
+- **process_debug_data.py**: `create_state_ranges()` and `process_debug_data()` for
+  converting raw debug output into DataFrames.
+- **utilities.py**: Model manipulation helpers (`extract_factors`, `remove_factors`,
+  `update_parameter_values`, `switch_translog_to_linear`, etc.).
+- **Visualization modules** (not in `__all__`, imported by module path):
+  `correlation_heatmap.py`, `visualize_factor_distributions.py`,
+  `visualize_transition_equations.py`, `utils_plotting.py`.
+
+### `get_maximization_inputs()` Return Dict
+
+Returns a dict with 6 keys:
+
+- `"loglike"`: `(params: pd.DataFrame) -> float` — jitted scalar log-likelihood
+- `"loglikeobs"`: `(params: pd.DataFrame) -> NDArray` — jitted per-observation
+  log-likelihood
+- `"debug_loglike"`: `(params: pd.DataFrame) -> dict` — non-jitted, returns dict with
+  keys `value`, `contributions`, `residuals`, `residual_sds`, `filtered_states`,
+  `state_ranges`, etc.
+- `"loglike_and_gradient"`: `(params: pd.DataFrame) -> tuple[float, NDArray]`
+- `"constraints"`: list of optimagic constraint objects
+- `"params_template"`: `pd.DataFrame` with correct MultiIndex and bounds; fixed
+  constraints pre-applied
+
+### ProcessedModel Key Attributes
+
+Applications frequently access these after calling `process_model(model_spec)`:
+
+- `processed.labels` — `.latent_factors`, `.observed_factors`, `.all_factors`,
+  `.controls`, `.stagemap`, `.stages`, `.aug_periods_to_periods`
+- `processed.dimensions` — `.n_periods`, `.n_latent_factors`
+- `processed.update_info` — DataFrame indexed by `(aug_period, variable)` with factor
+  columns and a `purpose` column
+- `processed.endogenous_factors_info` — `.has_endogenous_factors`,
+  `.aug_periods_from_period(period)`, `.factor_info`
+- `processed.normalizations` — dict of factor name to `Normalizations`
+- `processed.transition_info` — `.func` (vectorized), `.individual_functions[factor]`
 
 ### JAX Usage
 
@@ -90,28 +155,107 @@ The codebase uses:
 - `jax.vmap` for vectorization across observations
 - `jax.jit` for compilation
 - JAX arrays throughout the estimation pipeline
-- Optional GPU support via CUDA
+- Optional GPU support via CUDA or Metal
+
+### Public API (`__init__.py`)
+
+**Model specification classes:**
+
+- `ModelSpec`, `FactorSpec`, `AnchoringSpec`, `EstimationOptions`, `Normalizations`
 
-### Public API
+**Core estimation:**
 
-The main package exports three functions:
+- `get_maximization_inputs(model_spec, data, split_dataset=1)` — prepare optimization
+  problem
+- `get_filtered_states(model_spec, data, params)` — returns nested dict with
+  `"anchored_states"` and `"unanchored_states"`, each containing `"states"` (DataFrame)
+  and `"state_ranges"`
 
-- `get_maximization_inputs()`: Prepare optimization problem for parameter estimation
-- `get_filtered_states()`: Extract filtered latent factor estimates
-- `simulate_dataset()`: Generate synthetic data from model specification (accepts
-  optional `seed` parameter for reproducibility)
+**Simulation:**
+
+- `simulate_dataset(model_spec, params, n_obs=None, data=None, policies=None, seed=None)`
+  — returns dict with `"unanchored_states"`, `"anchored_states"`
+- `simulate_policy_effect(model_spec, params, data, policies, seed=None)` — returns
+  DataFrame of factor mean differences between policy and baseline
+
+**Diagnostics and visualization:**
+
+- `plot_likelihood_contributions(model_spec, data, params, period=None)`
+- `plot_residual_boxplots(model_spec, data, params, period=None)`
+- `decompose_measurement_variance(model_spec, params, data)` — returns DataFrame indexed
+  by `(period, measurement, factor)` with signal/noise columns
+- `summarize_measurement_reliability(variance_decomposition)`
+- `create_state_ranges(filtered_states, factors, quantile_cutoff=None)`
+
+### Frequently Used Internal APIs
+
+These are not in `__all__` but are imported directly by application projects:
+
+- `skillmodels.process_model.process_model` — central to all application code
+- `skillmodels.types.ProcessedModel`, `EndogenousFactorsInfo`
+- `skillmodels.decorators.register_params` — essential for custom transition functions
+- `skillmodels.constraints.constraints_dicts_to_om`, `enforce_fixed_constraints`
+- `skillmodels.utilities.extract_factors`, `update_parameter_values`
+- `skillmodels.process_data.pre_process_data`
+- `skillmodels.correlation_heatmap.get_measurements_corr`, `get_quasi_scores_corr`,
+  `get_scores_corr`, `plot_correlation_heatmap`
+- `skillmodels.visualize_factor_distributions.univariate_densities`,
+  `bivariate_density_contours`, `combine_distribution_plots`
+- `skillmodels.visualize_transition_equations.get_transition_plots`,
+  `combine_transition_plots`
+- `skillmodels.parse_params.create_parsing_info`, `parse_params`
+- `skillmodels.params_index.get_params_index`
 
 ## Code Style
 
 - Require Python 3.14
 - Uses Ruff for linting (target: Python 3.14, line length: 88)
-- Google-style docstrings
+- Google-style docstrings with imperative mood ("Return" not "Returns")
+- Use MyST syntax in docstrings (single backticks `like this`), not reStructuredText (no
+  double backticks, no `:ref:`, `:func:`, etc.)
+- Dataclass attributes use inline docstrings (docstring on the line after the field):
+  ```python
+  name: str
+  """Description of name."""
+  ```
 - Pre-commit hooks enforce formatting and linting
 - Type checking via `ty` with strict rules
 - Do not use `from __future__ import annotations`
 - Use modern numpy random API: `rng = np.random.default_rng(seed)` instead of
   `np.random.seed()` or legacy functions like `np.random.randn()`
 
+### Immutability Conventions
+
+- All model configuration and internal data structures use frozen dataclasses
+- Dict fields on internal dataclasses use `MappingProxyType` (not `Mapping`); wrap at
+  the call site with `MappingProxyType(...)`
+- Dict fields on user-facing dataclasses (`AnchoringSpec`, `Normalizations`) use
+  `Mapping` with `__post_init__` conversion via `ensure_containers_are_immutable()`
+- List fields use `tuple`, set fields use `frozenset`
+- `ensure_containers_are_immutable()` recursively converts dict→MappingProxyType,
+  list→tuple, set→frozenset
+
+### Period vs Aug_period
+
+Models with endogenous factors split each calendar period into multiple **augmented
+periods** (`aug_period`). The public API uses `period` (user-facing); `aug_period` is
+strictly internal. All public functions now return `period`:
+
+- `ModelSpec` — clean, no `aug_period` exposure.
+- `get_transition_plots()` — clean, accepts `period`/`periods`.
+- `get_filtered_states()` — clean, returns `period` column.
+- `simulate_dataset()` — clean, returns `period` in states DataFrames.
+- `plot_residual_boxplots()` / `plot_likelihood_contributions()` — clean, accept and
+  return `period`.
+- `decompose_measurement_variance()` — clean, indexed by
+  `(period, measurement, factor)`.
+- `simulate_policy_effect()` / `simulate_dataset()` policies — accept `"period"` key.
+- `ProcessedModel.labels` — exposes `aug_periods_to_periods` mapping (acceptable for
+  internal/advanced use).
+
+When writing new public-facing code, always accept and return `period`. Convert to
+`aug_period` internally using `ProcessedModel.labels.aug_periods_to_periods`.
+
 ## Testing
 
 - pytest with markers: `wip`, `unit`, `integration`, `end_to_end`
diff --git a/pixi.lock b/pixi.lock
index 761c5859..08153c98 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -9,38 +9,36 @@ environments:
       pypi-prerelease-mode: if-necessary-or-explicit
     packages:
       linux-64:
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.12.1-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py314h5bd0f2a_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.5-pyh29332c3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.2.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.3.0-py314h680f03e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.14.3-pyha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_105.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_105.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-h5f6438b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45.1-default_hfdba357_101.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45.1-default_h4852527_101.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.3.0-pyhcf101f3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-with-css-6.3.0-hbca2aae_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.2.0-hed03a55_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.1.4-hbd8a1cb_0.conda
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   - __osx >=11.0
   - libcxx >=19
-  - libsodium >=1.0.20,<1.0.21.0a0
-  - krb5 >=1.21.3,<1.22.0a0
+  - krb5 >=1.22.2,<1.23.0a0
+  - libsodium >=1.0.21,<1.0.22.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 244772
-  timestamp: 1757371008525
-- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h5bddc39_9.conda
-  sha256: 690cf749692c8ea556646d1a47b5824ad41b2f6dfd949e4cdb6c44a352fcb1aa
-  md5: a6c8f8ee856f7c3c1576e14b86cd8038
+  size: 245246
+  timestamp: 1772476886668
+- conda: https://conda.anaconda.org/conda-forge/win-64/zeromq-4.3.5-h507cc87_10.conda
+  sha256: b8568dfde46edf3455458912ea6ffb760e4456db8230a0cf34ecbc557d3c275f
+  md5: 1ab0237036bfb14e923d6107473b0021
   depends:
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - ucrt >=10.0.20348.0
-  - libsodium >=1.0.20,<1.0.21.0a0
-  - krb5 >=1.21.3,<1.22.0a0
+  - libsodium >=1.0.21,<1.0.22.0a0
+  - krb5 >=1.22.2,<1.23.0a0
   license: MPL-2.0
   license_family: MOZILLA
   purls: []
-  size: 265212
-  timestamp: 1757370864284
+  size: 265665
+  timestamp: 1772476832995
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
   sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
   md5: 30cd29cb87d819caead4d55184c1d115
@@ -9928,12 +10307,12 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/zipp?source=compressed-mapping
+  - pkg:pypi/zipp?source=hash-mapping
   size: 24194
   timestamp: 1764460141901
-- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.2-hceb46e0_1.conda
-  sha256: f2b6a175677701a0b6ce556b3bd362dc94a4e36ffcd10e3860e52ca036b4ad96
-  md5: 40feea2979654ed579f1cda7c63ccb94
+- conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-ng-2.3.3-hceb46e0_1.conda
+  sha256: ea4e50c465d70236408cb0bfe0115609fd14db1adcd8bd30d8918e0291f8a75f
+  md5: 2aadb0d17215603a82a2a6b0afd9a4cb
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -9941,22 +10320,22 @@ packages:
   license: Zlib
   license_family: Other
   purls: []
-  size: 122303
-  timestamp: 1766076745735
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.2-hed4e4f5_1.conda
-  sha256: ab481487381a6a6213d667e883252e52b8ca867b3b466c31a058126f964efffe
-  md5: 75f39a44c08cb5dc4ea847698de34ba3
+  size: 122618
+  timestamp: 1770167931827
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-ng-2.3.3-hed4e4f5_1.conda
+  sha256: a339606a6b224bb230ff3d711e801934f3b3844271df9720165e0353716580d4
+  md5: d99c2a23a31b0172e90f456f580b695e
   depends:
   - __osx >=11.0
   - libcxx >=19
   license: Zlib
   license_family: Other
   purls: []
-  size: 94882
-  timestamp: 1766076931977
-- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.2-h0261ad2_1.conda
-  sha256: e058e925bed8d9e5227cecc098e02992813046fd89206194435e975a9f6eff56
-  md5: bc2fba648e1e784c549e20bbe1a8af40
+  size: 94375
+  timestamp: 1770168363685
+- conda: https://conda.anaconda.org/conda-forge/win-64/zlib-ng-2.3.3-h0261ad2_1.conda
+  sha256: 71332532332d13b5dbe57074ddcf82ae711bdc132affa5a2982a29ffa06dc234
+  md5: 46a21c0a4e65f1a135251fc7c8663f83
   depends:
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
@@ -9964,8 +10343,8 @@ packages:
   license: Zlib
   license_family: Other
   purls: []
-  size: 123890
-  timestamp: 1766076739436
+  size: 124542
+  timestamp: 1770167984883
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
   sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7
   md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829
diff --git a/pyproject.toml b/pyproject.toml
index 6144dd45..5a29b2c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,11 +1,20 @@
-# ======================================================================================
-# Project metadata
-# ======================================================================================
+[build-system]
+build-backend = "hatchling.build"
+requires = [ "hatch-vcs", "hatchling" ]
 
 [project]
-authors = [
-  { name = "Janoś Gabler", email = "janos.gabler@gmail.com" },
+name = "skillmodels"
+description = "Estimators for skill formation models"
+readme = { file = "README.md", content-type = "text/markdown" }
+keywords = [
+  "Econometrics",
+  "Economics",
+  "Estimation",
+  "Skill formation",
+  "Statistics",
 ]
+license = { file = "LICENSE" }
+requires-python = ">=3.14,<3.15"
 classifiers = [
   "Development Status :: 4 - Beta",
   "Intended Audience :: Science/Research",
@@ -14,74 +23,135 @@ classifiers = [
   "Operating System :: Microsoft :: Windows",
   "Operating System :: POSIX",
   "Operating System :: Unix",
-  "Programming Language :: Python :: 3",
   "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3.14",
   "Topic :: Scientific/Engineering",
 ]
-dependencies = [
-  "dags",
-  "jax>=0.8",
-  "numpy",
-  "pandas",
-]
-description = "Estimators for skill formation models"
 dynamic = [ "version" ]
-keywords = [
-  "Skill formation",
-  "Econometrics",
-  "Economics",
-  "Estimation",
-  "Statistics",
-]
-license = { text = "MIT" }
-maintainers = [
-  { name = "Janoś Gabler", email = "janos.gabler@gmail.com" },
-  { name = "Hans-Martin von Gaudecker", email = "hmgaudecker@uni-bonn.de" },
+dependencies = [
+  "dags>=0.5",
+  "jax>=0.9",
+  "jupyter-book>=2",
+  "kaleido>=1.2",
+  "numpy>=2.4",
+  "optimagic>=0.5.3",
+  "pandas>=3",
+  "plotly>=6.6",
+  "pytask>=0.5.8",
+  "pytask-parallel>=0.5.2",
 ]
-name = "skillmodels"
-readme = { file = "README.md", content-type = "text/markdown" }
-requires-python = ">=3.14,<3.15"
-
+[[project.authors]]
+name = "Janoś Gabler"
+email = "janos.gabler@gmail.com"
+[[project.maintainers]]
+name = "Janoś Gabler"
+email = "janos.gabler@gmail.com"
+[[project.maintainers]]
+name = "Hans-Martin von Gaudecker"
+email = "hmgaudecker@uni-bonn.de"
 [project.urls]
 Github = "https://github.com/OpenSourceEconomics/skillmodels"
 Repository = "https://github.com/OpenSourceEconomics/skillmodels"
 Tracker = "https://github.com/OpenSourceEconomics/skillmodels/issues"
 
-# ======================================================================================
-# Build system configuration
-# ======================================================================================
-
-[build-system]
-build-backend = "hatchling.build"
-requires = [ "hatchling", "hatch-vcs" ]
-
-[tool.hatch.build.hooks.vcs]
-version-file = "src/skillmodels/_version.py"
-
-[tool.hatch.build.targets.sdist]
-exclude = [ "tests" ]
-only-packages = true
+[tool.hatch]
+build.hooks.vcs.version-file = "src/skillmodels/_version.py"
+build.targets.sdist.exclude = [ "tests" ]
+build.targets.sdist.only-packages = true
+build.targets.wheel.only-include = [ "src" ]
+build.targets.wheel.sources = [ "src" ]
+metadata.allow-direct-references = true
+version.source = "vcs"
 
-[tool.hatch.build.targets.wheel]
-only-include = [ "src" ]
-sources = [ "src" ]
-
-[tool.hatch.metadata]
-allow-direct-references = true
-
-[tool.hatch.version]
-source = "vcs"
-
-# ======================================================================================
-# Pixi configuration
-# ======================================================================================
-
-[tool.pixi.workspace]
-channels = [ "conda-forge" ]
-platforms = [ "linux-64", "osx-arm64", "win-64" ]
+[tool.ruff]
+fix = true
+unsafe-fixes = false
+[tool.ruff.lint]
+select = [ "ALL" ]
+extend-ignore = [
+  "COM812",  # Conflicts with ruff-format
+  "EM101",   # Exception must not use a string literal
+  "EM102",   # Exception must not use an f-string literal
+  "FBT002",  # Boolean default positional argument in function definition
+  "FIX002",  # Line contains TODO
+  "ISC001",  # Conflicts with ruff-format
+  "PERF401", # Use a list comprehension to create a transformed list
+  "PLR0913", # Too many arguments in function definition
+  "PLR2004", # Magic value used in comparison
+  "TC001",   # Move application import into a type-checking block
+  "TC002",   # Move third-party import into a type-checking block
+  "TC003",   # Move standard library import into a type-checking block
+  "TRY003",  # Long messages outside exception class
+]
+per-file-ignores."**/*.ipynb" = [
+  "B018",   # Found useless expression
+  "INP001", # File is part of an implicit namespace package
+  "T201",   # print found
+]
+per-file-ignores."tests/*" = [
+  "ANN",    # Missing type annotations
+  "ARG001", # Unused function argument
+  "D103",   # Missing docstring in public function
+  "FBT003", # Boolean positional value in function call
+  "INP001", # File is part of an implicit namespace package
+  "S101",   # Use of assert detected
+]
+pydocstyle.convention = "google"
+
+[tool.pyproject-fmt]
+column_width = 88
+max_supported_python = "3.14"
+table_format = "long"
+collapse_tables = [ "tool.hatch", "tool.pytest", "tool.pytask", "tool.ty" ]
+expand_tables = [
+  "tool.pixi.dependencies",
+  "tool.pixi.environments",
+  "tool.pixi.environments.cuda",
+  "tool.pixi.environments.docs",
+  "tool.pixi.environments.tests-cpu",
+  "tool.pixi.environments.tests-cuda",
+  "tool.pixi.environments.type-checking",
+  "tool.pixi.feature",
+  "tool.pixi.feature.cuda",
+  "tool.pixi.feature.cuda.dependencies",
+  "tool.pixi.feature.cuda.pypi-dependencies",
+  "tool.pixi.feature.cuda.system-requirements",
+  "tool.pixi.feature.cuda.tasks",
+  "tool.pixi.feature.docs",
+  "tool.pixi.feature.docs.dependencies",
+  "tool.pixi.feature.docs.tasks",
+  "tool.pixi.feature.tests",
+  "tool.pixi.feature.tests.dependencies",
+  "tool.pixi.feature.tests.target",
+  "tool.pixi.feature.tests.tasks",
+  "tool.pixi.feature.type-checking",
+  "tool.pixi.feature.type-checking.pypi-dependencies",
+  "tool.pixi.feature.type-checking.tasks",
+  "tool.pixi.pypi-dependencies",
+  "tool.pixi.pypi-dependencies.optimagic",
+  "tool.pixi.pypi-dependencies.skillmodels",
+  "tool.pixi.workspace",
+]
 
-# Development Dependencies (conda)
-# --------------------------------------------------------------------------------------
+[tool.ty]
+rules.ambiguous-protocol-member = "error"
+rules.deprecated = "error"
+rules.division-by-zero = "error"
+rules.ignore-comment-unknown-rule = "error"
+rules.ineffective-final = "error"
+rules.invalid-enum-member-annotation = "error"
+rules.invalid-ignore-comment = "error"
+rules.invalid-legacy-positional-parameter = "error"
+rules.possibly-missing-submodule = "error"
+rules.redundant-cast = "error"
+rules.unused-awaitable = "error"
+rules.unused-ignore-comment = "error"
+
+[tool.pytest]
+ini_options.addopts = [ "--pdbcls=pdbp:Pdb" ]
+ini_options.filterwarnings = []
+ini_options.markers = [ "integration: integration tests requiring MODEL2 + data" ]
+ini_options.norecursedirs = [ "docs" ]
 
 [tool.pixi.dependencies]
 filterpy = "*"
@@ -89,172 +159,64 @@ ipykernel = "*"
 jupyterlab = "*"
 nbformat = "*"
 networkx = "*"
-numpy = ">=2.3,<2.4"
-# optimagic = "*"
-plotly = ">=6.2"
 prek = "*"
 pybaum = "*"
 python = "~=3.14.0"
-python-kaleido = ">=1.0"
 scipy = "*"
-
-[tool.pixi.pypi-dependencies]
-optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "main" }
-pdbp = "*"
-skillmodels = { path = ".", editable = true }
-
-# Features and Tasks
-# --------------------------------------------------------------------------------------
-
+[tool.pixi.environments]
+cuda = { features = [ "cuda" ], solve-group = "cuda" }
+docs = { features = [ "docs" ], solve-group = "default" }
+tests-cpu = { features = [ "tests" ], solve-group = "default" }
+tests-cuda = { features = [ "tests", "cuda" ], solve-group = "cuda" }
+type-checking = { features = [ "type-checking" ], solve-group = "default" }
 [tool.pixi.feature.cuda]
 platforms = [ "linux-64" ]
 system-requirements = { cuda = "12" }
-
 [tool.pixi.feature.cuda.dependencies]
 cuda-nvcc = ">=12"
-
 [tool.pixi.feature.cuda.pypi-dependencies]
 jax = { version = ">=0.7", extras = [ "cuda12" ] }
-
-[tool.pixi.feature.test.dependencies]
+[tool.pixi.feature.cuda.tasks]
+mem-cuda = """\
+  pytest -x -s --pdb --memray --fail-on-increase \
+  tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs\
+  """
+[tool.pixi.feature.docs.tasks]
+build-docs = { cmd = "jupyter book build --html", cwd = "docs" }
+view-docs = { cmd = "jupyter book start", cwd = "docs" }
+[tool.pixi.feature.tests.dependencies]
 pytest = "*"
 pytest-cov = "*"
 pytest-xdist = "*"
 snakeviz = "*"
-
-[tool.pixi.feature.test.target.unix.dependencies]
+[tool.pixi.feature.tests.target.unix.dependencies]
 pytest-memray = "*"
-
-[tool.pixi.feature.test.tasks]
+[tool.pixi.feature.tests.tasks]
 tests = "pytest tests"
 tests-with-cov = "pytest tests --cov-report=xml --cov=./"
-mem = "pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
-mem-on-clean-repo = "git status --porcelain && git diff-index --quiet HEAD -- && git rev-parse HEAD && pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
-
-[tool.pixi.feature.cuda.tasks]
-mem-cuda = "pytest -x -s --pdb --memray --fail-on-increase tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs"
-
-[tool.pixi.feature.ty.pypi-dependencies]
-matplotlib = "*"   # required because of pandas
+mem = """\
+  pytest -x -s --pdb --memray --fail-on-increase \
+  tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs\
+  """
+mem-on-clean-repo = """\
+  git status --porcelain && git diff-index --quiet HEAD -- && git rev-parse HEAD && \
+  pytest -x -s --pdb --memray --fail-on-increase \
+  tests/test_likelihood_regression.py::test_likelihood_contributions_large_nobs\
+  """
+[tool.pixi.feature.type-checking.pypi-dependencies]
+matplotlib = "*"  # required because of pandas
 pandas-stubs = "*"
 ty = "*"
 types-pytz = "*"
-
-[tool.pixi.feature.ty.tasks]
+[tool.pixi.feature.type-checking.tasks]
 ty = "ty check src tests docs"
-
-[tool.pixi.feature.docs.dependencies]
-jupyter-book = ">=2.0"
-
-[tool.pixi.feature.docs.tasks]
-docs = { cmd = "jupyter book build --html", cwd = "docs" }
-view-docs = { cmd = "jupyter book start", cwd = "docs" }
-
-# Environments
-# --------------------------------------------------------------------------------------
-
-[tool.pixi.environments]
-cuda = { features = [ "cuda" ], solve-group = "cuda" }
-docs = { features = [ "docs" ], solve-group = "default" }
-test-cpu = { features = [ "test" ], solve-group = "default" }
-test-gpu = { features = [ "test", "cuda" ], solve-group = "cuda" }
-ty = { features = [ "test", "ty" ], solve-group = "default" }
-
-# ======================================================================================
-# Ruff configuration
-# ======================================================================================
-
-[tool.ruff]
-fix = true
-line-length = 88
-target-version = "py314"
-unsafe-fixes = false
-
-[tool.ruff.lint]
-extend-ignore = [
-  "ANN401",  # Dynamically typed expressions (typing.Any) are disallowed - too strict
-  "COM812",  # Conflicts with ruff-format
-  "EM101",   # Exception must not use a string literal
-  "EM102",   # Exception must not use an f-string literal
-  "FBT002",  # Boolean default positional argument in function definition
-  "FIX002",  # Line contains TODO
-  "ISC001",  # Conflicts with ruff-format
-  "PD015",   # pd.merge is fine
-  "PERF401", # Many suggestions to use list comprehension are not helpful
-  "PLR0913", # Too many arguments to function call
-  "PLR2004", # Magic values are fine
-  "S301",    # `pickle` module is unsafe
-  "TC001",   # Move application import into a type-checking block
-  "TC002",   # Move third-party import into a type-checking block
-  "TC003",   # Move standard library import into a type-checking block
-  "TRY003",  # Long messages outside exception class
-]
-select = [ "ALL" ]
-
-[tool.ruff.lint.per-file-ignores]
-"src/skillmodels/types.py" = [ "TC" ] # Dataclasses need types at runtime
-"src/skillmodels/visualize_*.py" = [ "BLE001" ]
-"**/*.ipynb" = [
-  "B018",   # Seemingly useless expression for printing.
-  "INP001", # No need for a namespace.
-  "T201",   # Printing is fine here.
-]
-"tests/*" = [
-  "ANN",    # No type annotations needed for tests
-  "ARG001", # Unused arguments are common in fixture-heavy tests
-  "D100",   # No module docstrings needed for tests
-  "D103",   # No function docstrings needed for tests
-  "E712",   # Comparison to True/False using == might be necessary for arrays.
-  "FBT003", # Boolean positional values are common in test setup
-  "INP001", # No need for a namespace.
-  "PT011",  # Broad pytest.raises() blocks are okay
-  "S101",   # use of `assert` detected
-]
-
-[tool.ruff.lint.pydocstyle]
-convention = "google"
-
-# ======================================================================================
-# ty configuration
-# ======================================================================================
-
-[tool.ty.rules]
-ambiguous-protocol-member = "error"
-deprecated = "error"
-division-by-zero = "error"
-ignore-comment-unknown-rule = "error"
-invalid-argument-type = "error"
-invalid-ignore-comment = "error"
-invalid-return-type = "error"
-possibly-missing-attribute = "error"
-possibly-missing-implicit-call = "error"
-possibly-missing-import = "error"
-possibly-unresolved-reference = "error"
-redundant-cast = "error"
-undefined-reveal = "error"
-unresolved-global = "error"
-unsupported-base = "error"
-unused-ignore-comment = "error"
-useless-overload-body = "error"
-
-# ======================================================================================
-# pytest configuration
-# ======================================================================================
-
-[tool.pytest.ini_options]
-addopts = [ "--pdbcls=pdbp:Pdb" ]
-filterwarnings = [  ]
-markers = [
-  "end_to_end: Flag for tests that cover the whole program.",
-  "integration: Flag for integration tests which may comprise of multiple unit tests.",
-  "unit: Flag for unit tests which target mainly a single function.",
-  "wip: Tests that are work-in-progress.",
-]
-norecursedirs = [ "docs", ".envs" ]
-
-# ======================================================================================
-# yamlfix configuration
-# ======================================================================================
+[tool.pixi.pypi-dependencies]
+# optimagic = { git = "https://github.com/optimagic-dev/optimagic.git", branch = "main" }
+pdbp = "*"
+skillmodels = { path = ".", editable = true }
+[tool.pixi.workspace]
+channels = [ "conda-forge" ]
+platforms = [ "linux-64", "osx-arm64", "win-64" ]
 
 [tool.yamlfix]
 line_length = 88
diff --git a/src/skillmodels/__init__.py b/src/skillmodels/__init__.py
index 42281478..c4fd82f4 100644
--- a/src/skillmodels/__init__.py
+++ b/src/skillmodels/__init__.py
@@ -5,6 +5,10 @@
 with contextlib.suppress(ImportError):
     import pdbp  # noqa: F401
 
+from skillmodels.diagnostic_plots import (
+    plot_likelihood_contributions,
+    plot_residual_boxplots,
+)
 from skillmodels.filtered_states import get_filtered_states
 from skillmodels.maximization_inputs import get_maximization_inputs
 from skillmodels.model_spec import (
@@ -14,7 +18,12 @@
     ModelSpec,
     Normalizations,
 )
-from skillmodels.simulate_data import simulate_dataset
+from skillmodels.process_debug_data import create_state_ranges
+from skillmodels.simulate_data import simulate_dataset, simulate_policy_effect
+from skillmodels.variance_decomposition import (
+    decompose_measurement_variance,
+    summarize_measurement_reliability,
+)
 
 __all__ = [
     "AnchoringSpec",
@@ -22,7 +31,13 @@
     "FactorSpec",
     "ModelSpec",
     "Normalizations",
+    "create_state_ranges",
+    "decompose_measurement_variance",
     "get_filtered_states",
     "get_maximization_inputs",
+    "plot_likelihood_contributions",
+    "plot_residual_boxplots",
     "simulate_dataset",
+    "simulate_policy_effect",
+    "summarize_measurement_reliability",
 ]
diff --git a/src/skillmodels/clipping.py b/src/skillmodels/clipping.py
index ce8dd000..0d2639af 100644
--- a/src/skillmodels/clipping.py
+++ b/src/skillmodels/clipping.py
@@ -25,8 +25,8 @@ def soft_clipping(
     `article by John Cook: <https://www.johndcook.com/soft_maximum.pdf>`_
 
     Note that contrary to the name, the soft maximum can be calculated using
-    ``scipy.special.logsumexp``. ``scipy.special.softmax`` is the gradient of
-    ``scipy.special.logsumexp``.
+    `scipy.special.logsumexp`. `scipy.special.softmax` is the gradient of
+    `scipy.special.logsumexp`.
 
     Args:
         arr: Array that is clipped elementwise.
diff --git a/src/skillmodels/constraints.py b/src/skillmodels/constraints.py
index 0d2df137..9319da63 100644
--- a/src/skillmodels/constraints.py
+++ b/src/skillmodels/constraints.py
@@ -246,7 +246,7 @@ def _get_stage_constraints(
 
 
 def _get_constant_factors_constraints(labels: Labels) -> list[dict]:
-    """Fix shock variances of constant factors to `bounds_distance`.
+    """Fix shock variances of constant factors to zero.
 
     Args:
         labels: Dict of lists with labels for the model quantities like
@@ -461,7 +461,8 @@ def _get_constraints_for_augmented_periods(
     return constraints_dicts
 
 
-def _sel(params: pd.DataFrame, loc: Any) -> pd.DataFrame:
+def select_by_loc(params: pd.DataFrame, loc: Any) -> pd.DataFrame:  # noqa: ANN401
+    """Select parameters by location."""
     return params.loc[loc]
 
 
@@ -547,12 +548,14 @@ def constraints_dicts_to_om(
         if c_d["type"] == "pairwise_equality":
             om_style.append(
                 SkillmodelsPairwiseEqualityConstraint(
-                    selectors=[functools.partial(_sel, loc=loc) for loc in c_d["loc"]],
+                    selectors=[
+                        functools.partial(select_by_loc, loc=loc) for loc in c_d["loc"]
+                    ],
                     **c_d,
                 )
             )
         else:
-            sel = functools.partial(_sel, loc=c_d["loc"])
+            sel = functools.partial(select_by_loc, loc=c_d["loc"])
             if c_d["type"] == "fixed":
                 om_style.append(SkillmodelsFixedConstraint(selector=sel, **c_d))
             elif c_d["type"] == "equality":
diff --git a/src/skillmodels/correlation_heatmap.py b/src/skillmodels/correlation_heatmap.py
index b1f98d89..be0f6aff 100644
--- a/src/skillmodels/correlation_heatmap.py
+++ b/src/skillmodels/correlation_heatmap.py
@@ -807,7 +807,7 @@ def _process_factors(
         observed_factors = tuple(
             fac for fac in factors if fac in model.labels.observed_factors
         )
-    return latent_factors, observed_factors  # ty: ignore[possibly-unresolved-reference]
+    return latent_factors, observed_factors
 
 
 def _process_periods(
diff --git a/src/skillmodels/decorators.py b/src/skillmodels/decorators.py
index 53060d00..348f46bc 100644
--- a/src/skillmodels/decorators.py
+++ b/src/skillmodels/decorators.py
@@ -32,7 +32,7 @@ def decorator_extract_params(func: Callable) -> Callable:
         if key is not None and names is None:
 
             @functools.wraps(func)
-            def wrapper_extract_params(**kwargs: Any) -> Any:
+            def wrapper_extract_params(**kwargs: Any) -> Any:  # noqa: ANN401
                 internal_kwargs = kwargs.copy()
                 internal_kwargs["params"] = kwargs["params"][key]
                 return func(**internal_kwargs)
@@ -40,7 +40,7 @@ def wrapper_extract_params(**kwargs: Any) -> Any:
         elif key is None and names is not None:
 
             @functools.wraps(func)
-            def wrapper_extract_params(**kwargs: Any) -> Any:
+            def wrapper_extract_params(**kwargs: Any) -> Any:  # noqa: ANN401
                 internal_kwargs = kwargs.copy()
                 internal_kwargs["params"] = dict(
                     zip(names, kwargs["params"], strict=False)
@@ -50,7 +50,7 @@ def wrapper_extract_params(**kwargs: Any) -> Any:
         elif key is not None and names is not None:
 
             @functools.wraps(func)
-            def wrapper_extract_params(**kwargs: Any) -> Any:
+            def wrapper_extract_params(**kwargs: Any) -> Any:  # noqa: ANN401
                 internal_kwargs = kwargs.copy()
                 internal_kwargs["params"] = dict(
                     zip(names, kwargs["params"][key], strict=False)
@@ -71,7 +71,7 @@ def jax_array_output(func: Callable) -> Callable:
     """Convert tuple output to list output."""
 
     @functools.wraps(func)
-    def wrapper_jax_array_output(*args: Any, **kwargs: Any) -> Array:
+    def wrapper_jax_array_output(*args: Any, **kwargs: Any) -> Array:  # noqa: ANN401
         raw = func(*args, **kwargs)
         return jnp.array(raw)
 
diff --git a/src/skillmodels/diagnostic_plots.py b/src/skillmodels/diagnostic_plots.py
new file mode 100644
index 00000000..21d45634
--- /dev/null
+++ b/src/skillmodels/diagnostic_plots.py
@@ -0,0 +1,248 @@
+"""Functions to create diagnostic plots for model evaluation."""
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+
+from skillmodels.maximization_inputs import get_maximization_inputs
+from skillmodels.model_spec import ModelSpec
+from skillmodels.process_model import process_model
+
+
+def plot_residual_boxplots(
+    model_spec: ModelSpec,
+    data: pd.DataFrame,
+    params: pd.DataFrame,
+    period: int | None = None,
+    *,
+    show_reference_line: bool = True,
+    layout_kwargs: dict[str, Any] | None = None,
+) -> go.Figure | dict[int, go.Figure]:
+    """Create boxplots of measurement residuals by measurement variable.
+
+    Residuals are computed as the difference between observed measurements and
+    their predicted values based on filtered states.
+
+    Args:
+        model_spec: The model specification.
+        data: Empirical dataset used to estimate the model.
+        params: Estimated model parameters.
+        period: If provided, create a single figure for that period. If None,
+            returns a dictionary mapping periods to figures.
+        show_reference_line: Whether to show a horizontal reference line at zero.
+        layout_kwargs: Dictionary of keyword arguments for Plotly layout.
+
+    Returns:
+        If period is specified, returns a single go.Figure. Otherwise, returns
+        a dictionary mapping period numbers to figures.
+
+    """
+    max_inputs = get_maximization_inputs(model_spec=model_spec, data=data)
+    # debug_loglike already returns processed debug data
+    processed_debug = max_inputs["debug_loglike"](params)
+
+    processed_model = process_model(model_spec)
+
+    residuals_df = processed_debug["residuals"]
+    update_info = processed_model.update_info
+
+    # Get period column name
+    period_col = "aug_period"
+
+    # Map measurement names
+    residuals_df = residuals_df.merge(
+        update_info.reset_index()[["aug_period", "variable"]].rename(
+            columns={"variable": "measurement"}
+        ),
+        left_on=[period_col, "measurement"],
+        right_on=["aug_period", "measurement"],
+        how="left",
+    )
+
+    # Map aug_period → period for the public API
+    ap_to_p = processed_model.labels.aug_periods_to_periods
+
+    available_periods = sorted(residuals_df[period_col].unique())
+
+    if period is not None:
+        # Find aug_period(s) matching the requested period
+        aug_periods_for_period = [ap for ap, p in ap_to_p.items() if p == period]
+        aug_period = aug_periods_for_period[0] if aug_periods_for_period else period
+        return _create_residual_boxplot_for_period(
+            residuals_df=residuals_df,
+            period=aug_period,
+            period_col=period_col,
+            show_reference_line=show_reference_line,
+            layout_kwargs=layout_kwargs,
+        )
+
+    return {
+        ap_to_p.get(p, p): _create_residual_boxplot_for_period(
+            residuals_df=residuals_df,
+            period=p,
+            period_col=period_col,
+            show_reference_line=show_reference_line,
+            layout_kwargs=layout_kwargs,
+        )
+        for p in available_periods
+    }
+
+
+def _create_residual_boxplot_for_period(
+    residuals_df: pd.DataFrame,
+    period: int,
+    period_col: str,
+    *,
+    show_reference_line: bool,
+    layout_kwargs: dict[str, Any] | None,
+) -> go.Figure:
+    """Create a single residual boxplot figure for one period."""
+    period_data = residuals_df[residuals_df[period_col] == period]
+
+    measurements = period_data["measurement"].unique()
+
+    fig = go.Figure()
+
+    for measurement in measurements:
+        meas_residuals = period_data[period_data["measurement"] == measurement][
+            "residual"
+        ]
+        fig.add_trace(
+            go.Box(
+                y=meas_residuals,
+                name=str(measurement),
+                boxpoints=False,
+            )
+        )
+
+    if show_reference_line:
+        fig.add_hline(y=0, line_dash="dash", line_color="red", line_width=1)
+
+    default_layout = {
+        "title": f"Residuals by Measurement (Period {period})",
+        "xaxis_title": "Measurement",
+        "yaxis_title": "Residual",
+        "showlegend": False,
+    }
+
+    if layout_kwargs:
+        default_layout.update(layout_kwargs)
+
+    fig.update_layout(**default_layout)
+
+    return fig
+
+
+def plot_likelihood_contributions(
+    model_spec: ModelSpec,
+    data: pd.DataFrame,
+    params: pd.DataFrame,
+    period: int | None = None,
+    *,
+    layout_kwargs: dict[str, Any] | None = None,
+) -> go.Figure | dict[int, go.Figure]:
+    """Create boxplots of log-likelihood contributions by measurement.
+
+    Args:
+        model_spec: The model specification.
+        data: Empirical dataset used to estimate the model.
+        params: Estimated model parameters.
+        period: If provided, create a single figure for that period. If None,
+            returns a dictionary mapping periods to figures.
+        layout_kwargs: Dictionary of keyword arguments for Plotly layout.
+
+    Returns:
+        If period is specified, returns a single go.Figure. Otherwise, returns
+        a dictionary mapping period numbers to figures.
+
+    """
+    max_inputs = get_maximization_inputs(model_spec=model_spec, data=data)
+    # debug_loglike already returns processed debug data
+    processed_debug = max_inputs["debug_loglike"](params)
+
+    processed_model = process_model(model_spec)
+
+    contributions_df = processed_debug["all_contributions"]
+    update_info = processed_model.update_info
+
+    period_col = "aug_period"
+
+    # Map measurement names
+    contributions_df = contributions_df.merge(
+        update_info.reset_index()[["aug_period", "variable"]].rename(
+            columns={"variable": "measurement"}
+        ),
+        left_on=[period_col, "measurement"],
+        right_on=["aug_period", "measurement"],
+        how="left",
+    )
+
+    # Map aug_period → period for the public API
+    ap_to_p = processed_model.labels.aug_periods_to_periods
+
+    available_periods = sorted(contributions_df[period_col].unique())
+
+    if period is not None:
+        # Find aug_period(s) matching the requested period
+        aug_periods_for_period = [ap for ap, p in ap_to_p.items() if p == period]
+        aug_period = aug_periods_for_period[0] if aug_periods_for_period else period
+        return _create_likelihood_boxplot_for_period(
+            contributions_df=contributions_df,
+            period=aug_period,
+            period_col=period_col,
+            layout_kwargs=layout_kwargs,
+        )
+
+    return {
+        ap_to_p.get(p, p): _create_likelihood_boxplot_for_period(
+            contributions_df=contributions_df,
+            period=p,
+            period_col=period_col,
+            layout_kwargs=layout_kwargs,
+        )
+        for p in available_periods
+    }
+
+
+def _create_likelihood_boxplot_for_period(
+    contributions_df: pd.DataFrame,
+    period: int,
+    period_col: str,
+    layout_kwargs: dict[str, Any] | None,
+) -> go.Figure:
+    """Create a single likelihood contribution boxplot figure for one period."""
+    period_data = contributions_df[contributions_df[period_col] == period]
+
+    measurements = period_data["measurement"].unique()
+
+    fig = go.Figure()
+
+    for measurement in measurements:
+        meas_contribs = period_data[period_data["measurement"] == measurement][
+            "contribution"
+        ]
+        # Filter out -inf values for visualization
+        meas_contribs = meas_contribs.replace([np.inf, -np.inf], np.nan).dropna()
+        fig.add_trace(
+            go.Box(
+                y=meas_contribs,
+                name=str(measurement),
+                boxpoints=False,
+            )
+        )
+
+    default_layout = {
+        "title": f"Log-Likelihood Contributions (Period {period})",
+        "xaxis_title": "Measurement",
+        "yaxis_title": "Log-Likelihood Contribution",
+        "showlegend": False,
+    }
+
+    if layout_kwargs:
+        default_layout.update(layout_kwargs)
+
+    fig.update_layout(**default_layout)
+
+    return fig
diff --git a/src/skillmodels/filtered_states.py b/src/skillmodels/filtered_states.py
index 308707f4..c525ac0e 100644
--- a/src/skillmodels/filtered_states.py
+++ b/src/skillmodels/filtered_states.py
@@ -35,10 +35,21 @@ def get_filtered_states(
         use_aug_period=True,
     )
 
+    # Map aug_period → period for the public API
+    ap_to_p = processed_model.labels.aug_periods_to_periods
+    for df in (anchored_states_df, unanchored_states_df):
+        df["period"] = df["aug_period"].map(ap_to_p)
+    anchored_states_df = anchored_states_df.drop(columns="aug_period")
+    unanchored_states_df = unanchored_states_df.drop(columns="aug_period")
+
     anchored_ranges = create_state_ranges(
         filtered_states=anchored_states_df,
         factors=processed_model.labels.latent_factors,
     )
+    unanchored_ranges = create_state_ranges(
+        filtered_states=unanchored_states_df,
+        factors=processed_model.labels.latent_factors,
+    )
 
     return {
         "anchored_states": {
@@ -103,17 +114,22 @@ def anchor_states_df(
     _scaling_factors = np.array(parsed_params.anchoring_scaling_factors[:, :n_latent])
     _constants = np.array(parsed_params.anchoring_constants[:, :n_latent])
     if use_aug_period:
+        # _scaling_factors is already indexed by aug_period, use directly
         period_arr = states_df["aug_period"].to_numpy()
-        ap_to_p = processed_model.labels.aug_periods_to_periods
-        scaling_factors = np.empty(shape=(len(ap_to_p), n_latent))
-        constants = np.empty(shape=(len(ap_to_p), n_latent))
-        for ap, p in ap_to_p.items():
-            scaling_factors[ap] = _scaling_factors[p]
-            constants[ap] = _constants[p]
-    else:
-        period_arr = states_df["period"].to_numpy()
         scaling_factors = _scaling_factors
         constants = _constants
+    else:
+        period_arr = states_df["period"].to_numpy()
+        ap_to_p = processed_model.labels.aug_periods_to_periods
+        n_periods = processed_model.dimensions.n_periods
+        scaling_factors = np.empty(shape=(n_periods, n_latent))
+        constants = np.empty(shape=(n_periods, n_latent))
+        for ap, p in ap_to_p.items():
+            # For endogenous models, multiple aug_periods map to the same
+            # period; constraints ensure they have identical anchoring params,
+            # so the last write per period is correct.
+            scaling_factors[p] = _scaling_factors[ap]
+            constants[p] = _constants[ap]
 
     scaling_arr = scaling_factors[period_arr]
     constants_arr = constants[period_arr]
diff --git a/src/skillmodels/kalman_filters.py b/src/skillmodels/kalman_filters.py
index 10bc034f..069fa9dd 100644
--- a/src/skillmodels/kalman_filters.py
+++ b/src/skillmodels/kalman_filters.py
@@ -15,9 +15,6 @@
 )
 
 
-# ======================================================================================
-# Update Step
-# ======================================================================================
 def kalman_update(
     states: Array,
     upper_chols: Array,
@@ -48,7 +45,6 @@ def kalman_update(
             normals distribution.
 
     Returns:
-        states: Same format as states.
         new_states: Same format as states.
         new_upper_chols: Same format as upper_chols
         new_log_mixture_weights: (jax.numpy.array): Same format as log_mixture_weights
@@ -134,9 +130,6 @@ def kalman_update(
     )
 
 
-# ======================================================================================
-# Predict Step
-# ======================================================================================
 def calculate_sigma_scaling_factor_and_weights(
     n_states: int,
     kappa: float = 2,
@@ -274,11 +267,9 @@ def _calculate_sigma_points(
     sigma_points = sigma_points.at[:, :, 1 : n_fac + 1].add(scaled_upper_chols)
     sigma_points = sigma_points.at[:, :, n_fac + 1 :].add(-scaled_upper_chols)
 
-    observed_part = observed_factors.repeat(n_sigma, axis=0).reshape(
-        n_obs,
-        n_mixtures,
-        n_sigma,
-        n_observed,
+    observed_part = jnp.broadcast_to(
+        observed_factors[:, jnp.newaxis, jnp.newaxis, :],
+        (n_obs, n_mixtures, n_sigma, n_observed),
     )
 
     return jnp.concatenate([sigma_points, observed_part], axis=-1)
diff --git a/src/skillmodels/kalman_filters_debug.py b/src/skillmodels/kalman_filters_debug.py
index 5d1ec44f..230b768d 100644
--- a/src/skillmodels/kalman_filters_debug.py
+++ b/src/skillmodels/kalman_filters_debug.py
@@ -9,9 +9,6 @@
 array_qr_jax = jax.vmap(jax.vmap(jnp.linalg.qr))
 
 
-# ======================================================================================
-# Update Step
-# ======================================================================================
 def kalman_update(
     states: Array,
     upper_chols: Array,
diff --git a/src/skillmodels/maximization_inputs.py b/src/skillmodels/maximization_inputs.py
index 3d376cb2..31d0cda3 100644
--- a/src/skillmodels/maximization_inputs.py
+++ b/src/skillmodels/maximization_inputs.py
@@ -246,7 +246,7 @@ def _partial_some_log_likelihood(
     )
 
 
-def _to_numpy(obj: Any) -> Any:
+def _to_numpy(obj: Any) -> Any:  # noqa: ANN401
     if isinstance(obj, dict):
         res = {}
         for key, value in obj.items():
diff --git a/src/skillmodels/model_spec.py b/src/skillmodels/model_spec.py
index a3eaaba1..9959c2cd 100644
--- a/src/skillmodels/model_spec.py
+++ b/src/skillmodels/model_spec.py
@@ -155,7 +155,7 @@ def factors(self) -> MappingProxyType[str, FactorSpec]:
         """Immutable mapping of factor names to specifications."""
         return self._factors
 
-    def _replace(self, **changes: Any) -> Self:
+    def _replace(self, **changes: Any) -> Self:  # noqa: ANN401
         """Return a new ModelSpec with the specified fields replaced."""
         return type(self)(
             factors=changes.get("factors", self.factors),
diff --git a/src/skillmodels/process_debug_data.py b/src/skillmodels/process_debug_data.py
index f8d055a0..770317d5 100644
--- a/src/skillmodels/process_debug_data.py
+++ b/src/skillmodels/process_debug_data.py
@@ -117,9 +117,10 @@ def _create_post_update_states(
     for (aug_period, meas), data in zip(
         update_info.index, filtered_states, strict=False
     ):
+        n_obs, n_mixtures_local, _ = data.shape
         df = _convert_state_array_to_df(arr=data, factor_names=factors)
         df["aug_period"] = aug_period
-        df["id"] = np.arange(len(df))
+        df["id"] = np.repeat(np.arange(n_obs), n_mixtures_local)
         df["measurement"] = meas
         to_concat.append(df)
 
@@ -176,13 +177,37 @@ def _create_filtered_states(
 def create_state_ranges(
     filtered_states: pd.DataFrame,
     factors: tuple[str, ...] | list[str],
+    quantile_cutoff: float | None = None,
 ) -> dict[str, pd.DataFrame]:
-    """Compute minimum and maximum state values for each factor by period."""
+    """Compute minimum and maximum state values for each factor by period.
+
+    Args:
+        filtered_states: DataFrame with filtered states. Must have a "period"
+            column.
+        factors: List of factor names to compute ranges for.
+        quantile_cutoff: If provided, use quantiles instead of min/max. The cutoff
+            is applied symmetrically: the minimum is the `quantile_cutoff` quantile
+            and the maximum is the `1 - quantile_cutoff` quantile. For example,
+            quantile_cutoff=0.01 uses the 1st and 99th percentiles.
+
+    Returns:
+        Dictionary mapping factor names to DataFrames with "minimum" and "maximum"
+        columns, indexed by period.
+
+    """
     ranges: dict[str, pd.DataFrame] = {}
     # Group by whichever period column is present
     period_col = "aug_period" if "aug_period" in filtered_states.columns else "period"
-    minima = filtered_states.groupby(period_col).min()
-    maxima = filtered_states.groupby(period_col).max()
+
+    if quantile_cutoff is not None:
+        if not 0 < quantile_cutoff < 0.5:
+            raise ValueError("quantile_cutoff must be between 0 and 0.5 (exclusive)")
+        minima = filtered_states.groupby(period_col).quantile(quantile_cutoff)
+        maxima = filtered_states.groupby(period_col).quantile(1 - quantile_cutoff)
+    else:
+        minima = filtered_states.groupby(period_col).min()
+        maxima = filtered_states.groupby(period_col).max()
+
     for factor in factors:
         df = pd.concat([minima[factor], maxima[factor]], axis=1)
         df.columns = pd.Index(["minimum", "maximum"])
@@ -200,7 +225,7 @@ def _process_residuals(
         df = pd.DataFrame(data.reshape(-1, 1), columns=["residual"])
         df["mixture"] = np.full((n_obs, n_mixtures), np.arange(n_mixtures)).flatten()
         df["aug_period"] = aug_period
-        df["id"] = np.arange(len(df))
+        df["id"] = np.repeat(np.arange(n_obs), n_mixtures)
         df["measurement"] = meas
         to_concat.append(df)
     return pd.concat(to_concat)
diff --git a/src/skillmodels/process_model.py b/src/skillmodels/process_model.py
index 89e2d069..43b5aedc 100644
--- a/src/skillmodels/process_model.py
+++ b/src/skillmodels/process_model.py
@@ -29,8 +29,6 @@
     TransitionInfo,
 )
 
-pd.set_option("future.no_silent_downcasting", True)  # noqa:  FBT003
-
 
 def process_model(model_spec: ModelSpec) -> ProcessedModel:
     """Check, clean, extend and transform the model specs.
@@ -112,9 +110,7 @@ def process_model(model_spec: ModelSpec) -> ProcessedModel:
     )
 
 
-def get_has_endogenous_factors(
-    factors: Mapping[str, FactorSpec],
-) -> bool:
+def get_has_endogenous_factors(factors: Mapping[str, FactorSpec]) -> bool:
     """Return True if any endogenous factors are present."""
     endogenous_factors = pd.DataFrame(
         [
@@ -499,6 +495,7 @@ def _get_update_info(
 
     for col in [c for c in uinfo.columns if c != "purpose"]:
         uinfo[col] = uinfo[col].fillna(value=False).astype(bool)
+    uinfo["purpose"] = uinfo["purpose"].astype(pd.StringDtype(na_value=np.nan))
     return uinfo
 
 
diff --git a/src/skillmodels/qr.py b/src/skillmodels/qr.py
index 566276b4..d42862f7 100644
--- a/src/skillmodels/qr.py
+++ b/src/skillmodels/qr.py
@@ -58,7 +58,7 @@ def qr_jvp_rule(
     primals: tuple[Array],
     tangents: tuple[Array],
 ) -> tuple[tuple[Array, Array], tuple[Array, Array]]:
-    """Calculates the derivative of the custom QR composition."""
+    """Calculate the derivative of the custom QR composition."""
     # See j-towns.github.io/papers/qr-derivative.pdf for a terse derivation.
     (x,) = primals
     (dx,) = tangents
diff --git a/src/skillmodels/simulate_data.py b/src/skillmodels/simulate_data.py
index b59554d4..13b9c005 100644
--- a/src/skillmodels/simulate_data.py
+++ b/src/skillmodels/simulate_data.py
@@ -44,8 +44,8 @@ def simulate_dataset(
         data: Dataset in the same format as for estimation, containing
             information about observed factors and control variables.
         policies: Each dictionary specifies a stochastic shock to a latent factor
-            AT THE END of "period" for "factor" with mean "effect_size" and
-            "standard deviation".
+            AT THE END of ``"period"`` for ``"factor"`` with mean
+            ``"effect_size"`` and ``"standard_deviation"``.
         seed: Random seed for reproducibility. If None, uses numpy's default random
             state.
 
@@ -93,9 +93,9 @@ def simulate_dataset(
         n_obs = data_n_obs
 
     else:
-        control_data = jnp.ones((n_obs, 1))
-        n_periods = processed_model.dimensions.n_periods
-        observed_factors = jnp.zeros((n_periods, n_obs, 0))
+        n_aug_periods = processed_model.dimensions.n_aug_periods
+        control_data = jnp.ones((n_aug_periods, n_obs, 1))
+        observed_factors = jnp.zeros((n_aug_periods, n_obs, 0))
 
     params_index = get_params_index(
         update_info=processed_model.update_info,
@@ -125,7 +125,14 @@ def simulate_dataset(
         n_obs=n_obs,
     )
 
-    aug_measurements, aug_latent_data = _simulate_dataset(
+    # Convert "period" keys in policies to "aug_period" for internal use
+    if policies is not None:
+        policies = _convert_policy_periods(
+            policies=policies,
+            endogenous_factors_info=processed_model.endogenous_factors_info,
+        )
+
+    _aug_measurements, aug_latent_data = _simulate_dataset(
         latent_states=states,
         covs=covs,
         log_weights=log_weights,
@@ -180,7 +187,6 @@ def simulate_dataset(
                 factors=processed_model.labels.latent_factors,
             ),
         },
-        "aug_measurements": aug_measurements,
     }
 
 
@@ -389,14 +395,38 @@ def _collapse_aug_periods_to_periods(
     is_states = df["_aug_period_meas_type"] == MeasurementType.STATES
 
     out = df.loc[is_endogenous, ["id", "period", *endogenous_cols]]
-    return pd.merge(
-        out,
+    return out.merge(
         df.loc[is_states, ["id", "period", *state_cols]],
         on=["id", "period"],
         how="outer",
     )
 
 
+def _convert_policy_periods(
+    policies: list[dict],
+    endogenous_factors_info: EndogenousFactorsInfo,
+) -> list[dict]:
+    """Convert ``"period"`` keys in policy dicts to ``"aug_period"``.
+
+    Policies may specify either ``"period"`` (public API) or ``"aug_period"``
+    (legacy/internal). This normalises to ``"aug_period"`` for the simulation loop.
+    """
+    converted = []
+    for policy in policies:
+        if "aug_period" in policy:
+            converted.append(policy)
+        elif "period" in policy:
+            p = dict(policy)
+            period = p.pop("period")
+            aug_periods = endogenous_factors_info.aug_periods_from_period(period)
+            # Use the first aug_period for the given period
+            p["aug_period"] = aug_periods[0]
+            converted.append(p)
+        else:
+            raise ValueError("Each policy dict must contain a 'period' key.")
+    return converted
+
+
 def _get_shock(
     rng: np.random.Generator,
     mean: float,
@@ -493,3 +523,78 @@ def measurements_from_states(
     states_part = np.dot(states, loadings.T)
     control_part = np.dot(controls, control_params.T)
     return states_part + control_part + epsilon
+
+
+def simulate_policy_effect(
+    model_spec: ModelSpec,
+    params: pd.DataFrame,
+    data: pd.DataFrame,
+    policies: list[dict],
+    seed: int | None = None,
+    *,
+    use_anchored_states: bool = True,
+) -> pd.DataFrame:
+    """Compute the effect of policies on factor means by period.
+
+    Simulates the model twice (with and without policies) and returns the
+    difference in factor means for each period.
+
+    Args:
+        model_spec: The model specification.
+        params: Model parameters.
+        data: Dataset with observed factors and control variables.
+        policies: List of policy dictionaries. Each dictionary specifies a
+            stochastic shock to a latent factor with keys:
+            - "period": When to apply the shock
+            - "factor": Which factor to shock
+            - "effect_size": Mean of the shock
+            - "standard_deviation": Standard deviation of the shock (use 0 for
+              deterministic effects)
+        seed: Random seed for reproducibility.
+        use_anchored_states: Whether to use anchored states for comparison.
+            Default True.
+
+    Returns:
+        DataFrame with the difference in factor means (policy - baseline) for
+        each period. Index is "period", columns are factor names.
+
+    Example:
+        >>> policies = [
+        ...     {"period": 1, "factor": "skill", "effect_size": 0.5,
+        ...      "standard_deviation": 0.0},
+        ... ]
+        >>> effect = simulate_policy_effect(model, params, data, policies)
+        >>> print(effect)  # Shows how much each factor changed due to policy
+
+    """
+    # Simulate baseline (no policy)
+    baseline = simulate_dataset(
+        model_spec=model_spec,
+        params=params,
+        data=data,
+        policies=None,
+        seed=seed,
+    )
+
+    # Simulate with policy
+    with_policy = simulate_dataset(
+        model_spec=model_spec,
+        params=params,
+        data=data,
+        policies=policies,
+        seed=seed,
+    )
+
+    state_key = "anchored_states" if use_anchored_states else "unanchored_states"
+
+    baseline_states = baseline[state_key]["states"]
+    policy_states = with_policy[state_key]["states"]
+
+    # Compute mean by period for each simulation
+    baseline_means = baseline_states.groupby("period").mean()
+    policy_means = policy_states.groupby("period").mean()
+
+    # Drop non-factor columns
+    factor_cols = [c for c in baseline_means.columns if c not in ("id", "period")]
+
+    return policy_means[factor_cols] - baseline_means[factor_cols]
diff --git a/src/skillmodels/test_data/model2_correct_params_index.csv b/src/skillmodels/test_data/model2_correct_params_index.csv
index 2ab896db..51c02800 100644
--- a/src/skillmodels/test_data/model2_correct_params_index.csv
+++ b/src/skillmodels/test_data/model2_correct_params_index.csv
@@ -1,4 +1,4 @@
-category,period,name1,name2
+category,aug_period,name1,name2
 controls,0,y1,constant
 controls,0,y1,x1
 controls,0,y2,constant
diff --git a/src/skillmodels/transition_functions.py b/src/skillmodels/transition_functions.py
index 9233557b..057ff965 100644
--- a/src/skillmodels/transition_functions.py
+++ b/src/skillmodels/transition_functions.py
@@ -12,7 +12,7 @@
         * states: 1d numpy array of length n_all_factors
         * params: 1d numpy array with coefficients specific to this transition function
 
-Returns:
+Return:
         * float
 **names_example_func(** *factors* **)**:
 
diff --git a/src/skillmodels/types.py b/src/skillmodels/types.py
index 97170382..05c8a795 100644
--- a/src/skillmodels/types.py
+++ b/src/skillmodels/types.py
@@ -11,7 +11,7 @@
 from jax import Array
 
 
-def _make_immutable(value: Any) -> Any:
+def _make_immutable(value: Any) -> Any:  # noqa: ANN401
     """Recursively convert a value to its immutable equivalent."""
     if isinstance(value, (MappingProxyType, tuple, frozenset)):
         return value
diff --git a/src/skillmodels/utilities.py b/src/skillmodels/utilities.py
index 37c2908d..9b69945f 100644
--- a/src/skillmodels/utilities.py
+++ b/src/skillmodels/utilities.py
@@ -55,7 +55,7 @@ def update_parameter_values(
         params: The params DataFrame for the full model.
         others: Another DataFrame with parameters or list
             of thereof. The values from other are used to update the value column
-            of ``params``. If other is a list, the updates will be in order, i.e.
+            of `params`. If other is a list, the updates will be in order, i.e.
             later elements overwrite earlier ones.
 
     Returns:
@@ -385,9 +385,8 @@ def _reduce_params(
     index = _get_params_index(model_spec)
     # If we have endogenous factors, we need to keep the periods from params.
     if has_endogenous_factors:
-        df = pd.merge(
-            left=params.reset_index(),
-            right=index.to_frame(index=False)[
+        df = params.reset_index().merge(
+            index.to_frame(index=False)[
                 ["category", "name1", "name2"]
             ].drop_duplicates(),
             on=["category", "name1", "name2"],
diff --git a/src/skillmodels/utils_plotting.py b/src/skillmodels/utils_plotting.py
index b66b89b8..b2f1b4fb 100644
--- a/src/skillmodels/utils_plotting.py
+++ b/src/skillmodels/utils_plotting.py
@@ -20,7 +20,7 @@ def get_layout_kwargs(
     title and legend.
 
     """
-    default_kwargs = {
+    default_kwargs: dict[str, Any] = {
         "template": "simple_white",
         "xaxis_showgrid": False,
         "yaxis_showgrid": False,
@@ -35,7 +35,7 @@ def get_layout_kwargs(
     if title_kwargs:
         default_kwargs["title"] = title_kwargs
     if legend_kwargs:
-        default_kwargs["legend"].update(legend_kwargs)  # ty: ignore[possibly-missing-attribute]
+        default_kwargs["legend"].update(legend_kwargs)
     if layout_kwargs:
         default_kwargs.update(layout_kwargs)
     return default_kwargs
diff --git a/src/skillmodels/variance_decomposition.py b/src/skillmodels/variance_decomposition.py
new file mode 100644
index 00000000..e92d055e
--- /dev/null
+++ b/src/skillmodels/variance_decomposition.py
@@ -0,0 +1,199 @@
+"""Functions for variance decomposition of measurements.
+
+Decomposes variance of observed measurements into signal (factor variance) and
+noise (measurement error) components following Cunha, Heckman, Schennach (2010),
+Section 4.2.2.
+"""
+
+from collections.abc import Mapping
+
+import pandas as pd
+
+from skillmodels.filtered_states import get_filtered_states
+from skillmodels.model_spec import ModelSpec
+from skillmodels.process_model import process_model
+
+
+def decompose_measurement_variance(
+    model_spec: ModelSpec,
+    params: pd.DataFrame,
+    data: pd.DataFrame,
+) -> pd.DataFrame:
+    """Decompose measurement variance into signal and noise components.
+
+    Based on Section 4.2.2 "The Empirical Importance of Measurement Error" of
+    Cunha, Heckman, Schennach (2010).
+
+    For each measurement y with loading L on factor F and measurement error sd:
+        Var(y) = L^2 * Var(F) + sd^2
+
+    This function computes:
+        - fraction_signal = L^2 * Var(F) / Var(y)
+        - fraction_noise = sd^2 / Var(y)
+
+    Args:
+        model_spec: The model specification.
+        params: DataFrame with estimated model parameters.
+        data: Empirical dataset used to estimate the model.
+
+    Returns:
+        DataFrame indexed by (period, measurement, factor) with columns:
+        - loading: The factor loading (L)
+        - factor_variance: Var(F) for that period
+        - meas_sd: The measurement error standard deviation
+        - fraction_signal: Fraction of variance due to factor (signal)
+        - fraction_noise: Fraction of variance due to measurement error (noise)
+        - signal_to_noise_ratio: Ratio of signal variance to noise variance
+
+    References:
+        Cunha, F., Heckman, J. J., & Schennach, S. M. (2010). Estimating the
+        Technology of Cognitive and Noncognitive Skill Formation. Econometrica,
+        78(3), 883-931. https://doi.org/10.3982/ECTA6551
+
+    """
+    # Get filtered states to compute factor variances
+    filtered_result = get_filtered_states(
+        model_spec=model_spec, data=data, params=params
+    )
+    filtered_states = filtered_result["anchored_states"]["states"]
+
+    processed_model = process_model(model_spec)
+    return _compute_variance_decomposition(
+        filtered_states=filtered_states,
+        params=params,
+        aug_periods_to_periods=processed_model.labels.aug_periods_to_periods,
+    )
+
+
+def _compute_variance_decomposition(
+    filtered_states: pd.DataFrame,
+    params: pd.DataFrame,
+    aug_periods_to_periods: Mapping[int, int],
+) -> pd.DataFrame:
+    """Compute variance decomposition from filtered states and parameters.
+
+    Args:
+        filtered_states: DataFrame with filtered states, must have columns for
+            each factor plus "period" and "id".
+        params: DataFrame with model parameters indexed by
+            (category, aug_period, name1, name2).
+        aug_periods_to_periods: Mapping from aug_period to period.
+
+    Returns:
+        DataFrame with variance decomposition results.
+
+    """
+    # Build reverse mapping: period → aug_period (pick first aug_period per period)
+    periods_to_aug_periods = {}
+    for ap, p in aug_periods_to_periods.items():
+        if p not in periods_to_aug_periods:
+            periods_to_aug_periods[p] = ap
+
+    # Add aug_period column for internal merges with params
+    filtered_states = filtered_states.copy()
+    filtered_states["aug_period"] = filtered_states["period"].map(
+        periods_to_aug_periods
+    )
+
+    # Compute factor variances by period
+    periods = filtered_states["aug_period"].unique()
+    factor_cols = [
+        c
+        for c in filtered_states.columns
+        if c not in ("aug_period", "period", "id", "mixture")
+    ]
+
+    factor_variances = {}
+    for period in periods:
+        period_data = filtered_states[filtered_states["aug_period"] == period]
+        factor_variances[period] = period_data[factor_cols].var()
+
+    variance_df = pd.DataFrame.from_dict(factor_variances, orient="index")
+    variance_df = variance_df.melt(
+        var_name="factor", value_name="factor_variance", ignore_index=False
+    ).reset_index(names="aug_period")
+
+    # Extract loadings (non-zero only)
+    loadings_df = params.loc["loadings"].reset_index()
+    loadings_df = loadings_df[loadings_df["value"] != 0].copy()
+    loadings_df = loadings_df.rename(
+        columns={"name1": "measurement", "name2": "factor", "value": "loading"}
+    )
+
+    # Merge loadings with factor variances
+    merged = loadings_df.merge(
+        variance_df,
+        on=["aug_period", "factor"],
+    )
+
+    # Extract measurement standard deviations
+    meas_sds_df = params.loc["meas_sds"].reset_index()
+    meas_sds_df = meas_sds_df.rename(
+        columns={"name1": "measurement", "value": "meas_sd"}
+    )
+    meas_sds_df = meas_sds_df[["aug_period", "measurement", "meas_sd"]]
+
+    # Merge with measurement SDs
+    merged = merged.merge(meas_sds_df, on=["aug_period", "measurement"])
+
+    # Validate measurement standard deviations
+    if (merged["meas_sd"] <= 0).any():
+        bad = merged.loc[merged["meas_sd"] <= 0, "meas_sd"]
+        raise ValueError(
+            f"meas_sd must be positive for variance decomposition, "
+            f"got non-positive values:\n{bad}",
+        )
+
+    # Compute variance decomposition
+    # Total variance of measurement: Var(y) = L^2 * Var(F) + sd^2
+    signal_var = merged["loading"] ** 2 * merged["factor_variance"]
+    noise_var = merged["meas_sd"] ** 2
+    total_var = signal_var + noise_var
+
+    merged["fraction_signal"] = signal_var / total_var
+    merged["fraction_noise"] = noise_var / total_var
+    merged["signal_to_noise_ratio"] = signal_var / noise_var
+
+    # Map aug_period → period for the public API
+    merged["period"] = merged["aug_period"].map(aug_periods_to_periods)
+
+    # Set index and select columns
+    return merged.set_index(["period", "measurement", "factor"])[
+        [
+            "loading",
+            "factor_variance",
+            "meas_sd",
+            "fraction_signal",
+            "fraction_noise",
+            "signal_to_noise_ratio",
+        ]
+    ]
+
+
+def summarize_measurement_reliability(
+    variance_decomposition: pd.DataFrame,
+) -> pd.DataFrame:
+    """Summarize measurement reliability across periods.
+
+    Args:
+        variance_decomposition: Output from decompose_measurement_variance.
+
+    Returns:
+        DataFrame indexed by measurement with summary statistics:
+        - mean_signal: Average fraction of variance due to signal
+        - min_signal: Minimum fraction due to signal
+        - max_signal: Maximum fraction due to signal
+
+    """
+    # Reset index to access columns
+    df = variance_decomposition.reset_index()
+
+    summary = (
+        df.groupby("measurement")["fraction_signal"]
+        .agg(["mean", "min", "max"])
+        .rename(
+            columns={"mean": "mean_signal", "min": "min_signal", "max": "max_signal"}
+        )
+    )
+
+    return summary.sort_values("mean_signal", ascending=False)
diff --git a/src/skillmodels/visualize_factor_distributions.py b/src/skillmodels/visualize_factor_distributions.py
index cdf7587a..c6ca7a9b 100644
--- a/src/skillmodels/visualize_factor_distributions.py
+++ b/src/skillmodels/visualize_factor_distributions.py
@@ -251,7 +251,7 @@ def univariate_densities(
         hist_data = [df[fac][df["scenario"] == s] for s in scenarios]
         try:
             fig = ff.create_distplot(hist_data, **distplot_kwargs)
-        except Exception as e:
+        except Exception as e:  # noqa: BLE001
             warnings.warn(
                 f"""Plotting univariate density failed for {fac} in
                 period {period} with error:\n\n{e}""",
@@ -376,7 +376,7 @@ def bivariate_density_contours(
                 )
                 fig.add_trace(contour)
                 fig.update_traces(**contour_kwargs)
-            except Exception as e:
+            except Exception as e:  # noqa: BLE001
                 warnings.warn(
                     f"""
                     Contour plot failed for {pair} in period {period}
@@ -494,7 +494,7 @@ def bivariate_density_surfaces(
                     opacity=opacity,
                 ),
             )
-        except Exception as e:
+        except Exception as e:  # noqa: BLE001
             warnings.warn(
                 f"""Plotting bivariate density surfaces for {pair} in
                 period {period} with error:\n\n{e}""",
@@ -559,10 +559,12 @@ def _process_data(
         data = pd.concat(to_concat, axis=1)
         data["scenario"] = "none"
     else:
-        if not isinstance(states, dict):
-            states = dict(enumerate(states))
+        if isinstance(states, dict):
+            states_dict = states
+        else:
+            states_dict = {str(i): df for i, df in enumerate(states)}
         to_concat = []
-        for name, df in states.items():
+        for name, df in states_dict.items():
             one_state_per_period = _get_one_state_per_period(states=df, ap_to_p=ap_to_p)
             to_keep = one_state_per_period.query(f"period == {period}")[factors].copy()
             to_keep["scenario"] = name
diff --git a/src/skillmodels/visualize_transition_equations.py b/src/skillmodels/visualize_transition_equations.py
index 9a8b6669..e74e51b0 100644
--- a/src/skillmodels/visualize_transition_equations.py
+++ b/src/skillmodels/visualize_transition_equations.py
@@ -1,9 +1,9 @@
 """Functions to visualize transition equations and production functions."""
 
 import itertools
-from collections.abc import Callable
+from collections.abc import Callable, Mapping, Sequence
 from copy import deepcopy
-from typing import Any
+from typing import Any, Literal
 
 import jax.numpy as jnp
 import numpy as np
@@ -140,22 +140,26 @@ def combine_transition_plots(
     return fig
 
 
-def get_transition_plots(
+def get_transition_plots(  # noqa: C901, PLR0912
     model_spec: ModelSpec,
     params: pd.DataFrame,
-    data: pd.DataFrame,
-    period: int,
+    data: pd.DataFrame | None = None,
+    period: int | None = None,
+    periods: Sequence[int] | None = None,
     state_ranges: dict[str, pd.DataFrame] | None = None,
     quantiles_of_other_factors: tuple[float, ...] | list[float] | float | None = (
         0.25,
         0.5,
         0.75,
     ),
+    aggregation_method: Literal["quantiles", "median", "average"] = "quantiles",
     n_points: int = 50,
     n_draws: int = 50,
-    colorscale: str = "Magenta_r",
+    colorscale: str | list[str] = "Magenta_r",
+    state_range_quantile_cutoff: float | None = None,
     layout_kwargs: dict[str, Any] | None = None,
     *,
+    states: pd.DataFrame | None = None,
     include_correction_factors: bool = False,
 ) -> dict[tuple[str, str], go.Figure]:
     """Get dictionary with individual plots of transition equations for each factor.
@@ -163,22 +167,37 @@ def get_transition_plots(
     Args:
         model_spec: The model specification. See: :ref:`model_specs`
         params: Model parameters.
-        data: Empirical dataset used to estimate the model.
+        data: Empirical dataset used to estimate the model. Required when `states`
+            is not provided or when the model has observed factors.
         period: The start period of the transition equations that are plotted.
+            Deprecated in favor of `periods`. If both are provided, `periods` is used.
+        periods: List of periods to overlay on each plot. Each period gets a different
+            color. If None and period is None, uses the first valid period.
         state_ranges: The keys are the names of the latent factors.
             The values are DataFrames with the columns "period", "minimum", "maximum".
             The state_ranges are used to define the axis limits of the plots.
         quantiles_of_other_factors: Quantiles at which the factors
-            that are not varied in a given plot are fixed. If None, those factors are
-            not fixed but integrated out.
+            that are not varied in a given plot are fixed. Only used when
+            aggregation_method is "quantiles". If None, those factors are
+            not fixed but integrated out (equivalent to aggregation_method="average").
+        aggregation_method: How to handle factors not being varied in a plot.
+            - "quantiles": Fix other factors at specified quantiles (default).
+            - "median": Fix other factors at their median (0.5 quantile).
+            - "average": Average over random draws of other factors.
         n_points: Number of grid points per input. Default 50.
         n_draws: Number of randomly drawn values of the factors that are averaged
-            out. Only relevant if quantiles_of_other_factors is *None*. Default 50.
-        colorscale: The color scale to use for line legends. Must be a valid
-            plotly.express.colors.sequential attribute. Default 'Magenta_r'.
+            out. Only relevant if aggregation_method is "average". Default 50.
+        colorscale: The color scale to use for line legends. Can be a string
+            (plotly.express.colors.sequential attribute) or a list of colors.
+            Default 'Magenta_r'.
+        state_range_quantile_cutoff: If provided, compute state ranges by cutting
+            this quantile from both tails (e.g., 0.01 cuts 1st and 99th percentiles).
+            Only used if state_ranges is None.
         layout_kwargs: Dictionary of key word arguments used to
             update layout of plotly image object. If None, the default kwargs
             defined in the function will be used.
+        states: Pre-computed filtered states DataFrame (with a `period`
+            column). If provided, skip the internal `get_filtered_states` call.
         include_correction_factors: Whether to include correction factors in the
             plots. Default False.
 
@@ -187,16 +206,38 @@ def get_transition_plots(
             for each combination of input and output factors.
 
     """
-    quantiles_of_other_factors = _process_quantiles_of_other_factors(
-        quantiles_of_other_factors,
-    )
+    # Handle period/periods arguments
+    if periods is not None:
+        periods_list = list(periods)
+    elif period is not None:
+        periods_list = [period]
+    else:
+        periods_list = None  # Will be set after processing model
+
+    # Convert aggregation_method to quantiles_of_other_factors format
+    if aggregation_method == "median":
+        quantiles_of_other_factors = [0.5]
+    elif aggregation_method == "average":
+        quantiles_of_other_factors = None
+    else:  # "quantiles"
+        quantiles_of_other_factors = _process_quantiles_of_other_factors(
+            quantiles_of_other_factors,
+        )
 
     processed_model = process_model(model_spec)
 
-    if period >= processed_model.labels.periods[-1]:
-        raise ValueError(
-            "*period* must be the penultimate period of the model or earlier.",
-        )
+    # Set default periods if not provided
+    if periods_list is None:
+        periods_list = [0]
+
+    # Validate periods
+    max_period = processed_model.labels.periods[-1]
+    for p in periods_list:
+        if p >= max_period:
+            raise ValueError(
+                f"Period {p} is invalid. Must be less than {max_period} "
+                "(the last period has no transition).",
+            )
 
     if (
         include_correction_factors
@@ -210,9 +251,19 @@ def get_transition_plots(
             if not processed_model.endogenous_factors_info.factor_info[lf].is_correction
         ]
     all_factors = processed_model.labels.all_factors
-    states = get_filtered_states(model_spec=model_spec, data=data, params=params)[
-        "anchored_states"
-    ]["states"]
+    if states is None:
+        if data is None:
+            msg = "Either 'data' or 'states' must be provided."
+            raise TypeError(msg)
+        states = get_filtered_states(model_spec=model_spec, data=data, params=params)[
+            "anchored_states"
+        ]["states"]
+
+    states = _normalize_states_columns(
+        states,
+        aug_periods_to_periods=processed_model.labels.aug_periods_to_periods,
+    )
+
     return _get_dictionary_with_plots(
         model=processed_model,
         data=data,
@@ -222,27 +273,29 @@ def get_transition_plots(
         latent_factors=latent_factors,  # ty: ignore[invalid-argument-type]
         all_factors=all_factors,
         quantiles_of_other_factors=quantiles_of_other_factors,
-        period=period,
+        periods=periods_list,
         n_points=n_points,
         n_draws=n_draws,
         colorscale=colorscale,
+        state_range_quantile_cutoff=state_range_quantile_cutoff,
         layout_kwargs=layout_kwargs,
     )
 
 
 def _get_dictionary_with_plots(
     model: ProcessedModel,
-    data: pd.DataFrame,
+    data: pd.DataFrame | None,
     params: pd.DataFrame,
     states: pd.DataFrame,
     state_ranges: dict[str, pd.DataFrame] | None,
     latent_factors: tuple[str, ...],
     all_factors: tuple[str, ...],
     quantiles_of_other_factors: list[float] | None,
-    period: int,
+    periods: list[int],
     n_points: int,
     n_draws: int,
-    colorscale: str,
+    colorscale: str | list[str],
+    state_range_quantile_cutoff: float | None,
     layout_kwargs: dict[str, Any] | None,
     *,
     showlegend: bool = True,
@@ -263,7 +316,6 @@ def _get_dictionary_with_plots(
         state_ranges: The keys are the names of the latent factors.
             The values are DataFrames with the columns "period", "minimum", "maximum".
             The state_ranges are used to define the axis limits of the plots.
-
         latent_factors: Latent factors of the model that are outputs of
             transition factors.
         all_factors: All factors of the model that are the inputs of transition
@@ -271,12 +323,14 @@ def _get_dictionary_with_plots(
         quantiles_of_other_factors: Quantiles at which the factors
             that are not varied in a given plot are fixed. If None, those factors are
             not fixed but integrated out.
-        period: The start period of the transition equations that are plotted.
+        periods: List of periods to plot. Each period gets a different line/color.
         n_points: Number of grid points per input. Default 50.
         n_draws: Number of randomly drawn values of the factors that are averaged
             out. Only relevant if quantiles_of_other_factors is *None*. Default 50.
-        colorscale: The color scale to use for line legends. Must be a valid
-            plotly.express.colors.sequential attribute. Default 'Magenta_r'.
+        colorscale: The color scale to use for line legends. Can be a string
+            (plotly.express.colors.sequential attribute) or a list of colors.
+        state_range_quantile_cutoff: If provided, compute state ranges by cutting
+            this quantile from both tails.
         layout_kwargs: Dictionary of key word arguments used to
             update layout of plotly image object. If None, the default kwargs defined
             in the function will be used.
@@ -288,17 +342,27 @@ def _get_dictionary_with_plots(
 
     """
     observed_factors = model.labels.observed_factors
-    states_data = _get_states_data(
-        model=model,
-        period=period,
-        data=data,
-        states=states,
-        observed_factors=observed_factors,
-    )
+
+    # Collect states data for all periods
+    all_states_data = []
+    for period in periods:
+        period_states = _get_states_data(
+            model=model,
+            period=period,
+            data=data,
+            states=states,
+            observed_factors=observed_factors,
+        )
+        all_states_data.append(period_states)
+    states_data = pd.concat(all_states_data, ignore_index=True)
+
     params = _set_index_params(model=model, params=params)
     parsed_params = _get_parsed_params(model=model, params=params)
     state_ranges = _get_state_ranges(
-        state_ranges=state_ranges, states_data=states_data, all_factors=all_factors
+        state_ranges=state_ranges,
+        states_data=states_data,
+        all_factors=all_factors,
+        quantile_cutoff=state_range_quantile_cutoff,
     )
     layout_kwargs = get_layout_kwargs(
         layout_kwargs=layout_kwargs,
@@ -306,84 +370,182 @@ def _get_dictionary_with_plots(
         title_kwargs=None,
         showlegend=showlegend,
     )
-    has_endogenous_factors = model.endogenous_factors_info.has_endogenous_factors
-    if has_endogenous_factors:
-        _aug_periods = model.endogenous_factors_info.aug_periods_from_period(period)
+
+    # Get color sequence
+    if isinstance(colorscale, str):
+        colors = getattr(px.colors.sequential, colorscale)
     else:
-        _aug_periods = [period]
+        colors = colorscale
+
     plots_dict = {}
     for output_factor, input_factor in itertools.product(latent_factors, all_factors):
-        transition_function = model.transition_info.individual_functions[output_factor]
-        if (
-            has_endogenous_factors
-            and model.endogenous_factors_info.factor_info[output_factor].is_endogenous
-        ):
-            aug_period = min(_aug_periods)
-        else:
-            aug_period = max(_aug_periods)
-        transition_params = {
-            output_factor: parsed_params.transition[output_factor][aug_period]
-        }
-
-        if quantiles_of_other_factors is not None:
-            plot_data = _prepare_data_for_one_plot_fixed_quantile_2d(
-                states_data=states_data,
-                state_ranges=state_ranges,
-                aug_period=aug_period,
-                input_factor=input_factor,
-                output_factor=output_factor,
-                n_points=n_points,
-                quantiles_of_other_factors=quantiles_of_other_factors,
-                transition_function=transition_function,
-                transition_params=transition_params,
-                all_factors=all_factors,
-            )
+        combined_data = _prepare_plot_data_for_factor_pair(
+            model=model,
+            states_data=states_data,
+            state_ranges=state_ranges,
+            parsed_params=parsed_params,
+            periods=periods,
+            input_factor=input_factor,
+            output_factor=output_factor,
+            all_factors=all_factors,
+            quantiles_of_other_factors=quantiles_of_other_factors,
+            n_points=n_points,
+            n_draws=n_draws,
+        )
 
-        else:
-            plot_data = _prepare_data_for_one_plot_average_2d(
-                states_data=states_data,
-                state_ranges=state_ranges,
-                aug_period=aug_period,
-                input_factor=input_factor,
-                output_factor=output_factor,
-                n_points=n_points,
-                n_draws=n_draws,
-                transition_function=transition_function,
-                transition_params=transition_params,
-                all_factors=all_factors,
-            )
+        color = _determine_color_column(len(periods), quantiles_of_other_factors)
 
-        if (
-            isinstance(quantiles_of_other_factors, list)
-            and len(quantiles_of_other_factors) > 1
-        ):
-            color = "quantile"
-        else:
-            color = None
         subfig = px.line(
-            plot_data,
+            combined_data,
             y=f"output_{output_factor}",
             x=f"input_{input_factor}",
             color=color,
-            color_discrete_sequence=getattr(px.colors.sequential, colorscale),
+            color_discrete_sequence=colors,
         )
         subfig.update_xaxes(title={"text": input_factor})
         subfig.update_yaxes(title={"text": output_factor})
+        subfig.update_traces(line={"width": 2})
         subfig.update_layout(**layout_kwargs)
         plots_dict[(input_factor, output_factor)] = deepcopy(subfig)
 
     return plots_dict
 
 
+def _prepare_plot_data_for_factor_pair(
+    model: ProcessedModel,
+    states_data: pd.DataFrame,
+    state_ranges: dict[str, pd.DataFrame],
+    parsed_params: ParsedParams,
+    periods: list[int],
+    input_factor: str,
+    output_factor: str,
+    all_factors: tuple[str, ...],
+    quantiles_of_other_factors: list[float] | None,
+    n_points: int,
+    n_draws: int,
+) -> pd.DataFrame:
+    """Prepare plot data for one input/output factor pair across all periods."""
+    transition_function = model.transition_info.individual_functions[output_factor]
+    has_endogenous_factors = model.endogenous_factors_info.has_endogenous_factors
+
+    period_data_frames = []
+    for period in periods:
+        aug_period = _get_aug_period_for_output(
+            model=model,
+            period=period,
+            output_factor=output_factor,
+            has_endogenous_factors=has_endogenous_factors,
+        )
+
+        transition_params = {
+            output_factor: parsed_params.transition[output_factor][aug_period]
+        }
+        period_states = states_data[states_data["aug_period"] == aug_period]
+
+        plot_data = _prepare_single_period_plot_data(
+            states_data=period_states,
+            state_ranges=state_ranges,
+            aug_period=aug_period,
+            input_factor=input_factor,
+            output_factor=output_factor,
+            all_factors=all_factors,
+            quantiles_of_other_factors=quantiles_of_other_factors,
+            n_points=n_points,
+            n_draws=n_draws,
+            transition_function=transition_function,
+            transition_params=transition_params,
+        )
+        plot_data["period"] = period
+        period_data_frames.append(plot_data)
+
+    return pd.concat(period_data_frames, ignore_index=True)
+
+
+def _get_aug_period_for_output(
+    model: ProcessedModel,
+    period: int,
+    output_factor: str,
+    *,
+    has_endogenous_factors: bool,
+) -> int:
+    """Determine the augmented period for an output factor."""
+    if not has_endogenous_factors:
+        return period
+
+    _aug_periods = model.endogenous_factors_info.aug_periods_from_period(period)
+    if model.endogenous_factors_info.factor_info[output_factor].is_endogenous:
+        return min(_aug_periods)
+    return max(_aug_periods)
+
+
+def _prepare_single_period_plot_data(
+    states_data: pd.DataFrame,
+    state_ranges: dict[str, pd.DataFrame],
+    aug_period: int,
+    input_factor: str,
+    output_factor: str,
+    all_factors: tuple[str, ...],
+    quantiles_of_other_factors: list[float] | None,
+    n_points: int,
+    n_draws: int,
+    transition_function: Callable[..., Array],
+    transition_params: dict[str, Any],
+) -> pd.DataFrame:
+    """Prepare plot data for a single period."""
+    if quantiles_of_other_factors is not None:
+        return _prepare_data_for_one_plot_fixed_quantile_2d(
+            states_data=states_data,
+            state_ranges=state_ranges,
+            aug_period=aug_period,
+            input_factor=input_factor,
+            output_factor=output_factor,
+            n_points=n_points,
+            quantiles_of_other_factors=quantiles_of_other_factors,
+            transition_function=transition_function,
+            transition_params=transition_params,
+            all_factors=all_factors,
+        )
+    return _prepare_data_for_one_plot_average_2d(
+        states_data=states_data,
+        state_ranges=state_ranges,
+        aug_period=aug_period,
+        input_factor=input_factor,
+        output_factor=output_factor,
+        n_points=n_points,
+        n_draws=n_draws,
+        transition_function=transition_function,
+        transition_params=transition_params,
+        all_factors=all_factors,
+    )
+
+
+def _determine_color_column(
+    n_periods: int,
+    quantiles_of_other_factors: list[float] | None,
+) -> str | None:
+    """Determine which column to use for color encoding."""
+    if n_periods > 1:
+        return "period"
+    if (
+        isinstance(quantiles_of_other_factors, list)
+        and len(quantiles_of_other_factors) > 1
+    ):
+        return "quantile"
+    return None
+
+
 def _get_state_ranges(
     state_ranges: dict[str, pd.DataFrame] | None,
     states_data: pd.DataFrame,
     all_factors: tuple[str, ...],
+    quantile_cutoff: float | None = None,
 ) -> dict[str, pd.DataFrame]:
     """Create state ranges if none is given."""
     if state_ranges is None:
         state_ranges = create_state_ranges(
-            filtered_states=states_data, factors=list(all_factors)
+            filtered_states=states_data,
+            factors=list(all_factors),
+            quantile_cutoff=quantile_cutoff,
         )
     return state_ranges
 
@@ -430,17 +592,17 @@ def _set_index_params(
 def _get_states_data(
     model: ProcessedModel,
     period: int,
-    data: pd.DataFrame,
+    data: pd.DataFrame | None,
     states: pd.DataFrame,
     observed_factors: tuple[str, ...],
 ) -> pd.DataFrame:
-    if observed_factors and data is None:
-        raise ValueError(
-            "The model has observed factors. You must pass the empirical data to "
-            "'visualize_transition_equations' via the keyword *data*.",
-        )
-
     if observed_factors:
+        if data is None:
+            msg = (
+                "The model has observed factors. You must pass the empirical data to "
+                "'get_transition_plots' via the keyword 'data'."
+            )
+            raise TypeError(msg)
         _observed_arr = process_data(
             df=data,
             has_endogenous_factors=model.endogenous_factors_info.has_endogenous_factors,
@@ -474,9 +636,8 @@ def _get_states_data(
             observed_data["id"] = observed_data.index
             observed_data["aug_period"] = period
         # Do a left merge because we need all periods for the ranges
-        states_data = pd.merge(
-            left=states,
-            right=observed_data,
+        states_data = states.merge(
+            observed_data,
             left_on=["id", "aug_period"],
             right_on=["id", "aug_period"],
             how="left",
@@ -486,6 +647,53 @@ def _get_states_data(
     return states_data
 
 
+def _normalize_states_columns(
+    states: pd.DataFrame,
+    aug_periods_to_periods: Mapping[int, int] | None = None,
+) -> pd.DataFrame:
+    """Ensure `aug_period` and `id` are columns, not index levels.
+
+    Pre-computed states DataFrames may carry period information as `period`
+    (in the index or a column) instead of `aug_period`.  Downstream code
+    uniformly expects `aug_period` as a column, so this helper promotes
+    index levels to columns and, when a mapping is provided, expands each
+    period row into one row per corresponding aug_period.
+
+    Args:
+        states: DataFrame with latent factor columns and either `period` or
+            `aug_period` identifying the time dimension.
+        aug_periods_to_periods: Mapping from aug_period to period.  When
+            provided and the DataFrame has `period` but not `aug_period`,
+            rows are expanded so that each period produces one row per
+            aug_period that maps to it.
+    """
+    # Promote relevant index levels to columns.
+    names_to_reset = [
+        n for n in states.index.names if n in ("period", "aug_period", "id")
+    ]
+    if names_to_reset:
+        states = states.reset_index(level=names_to_reset)
+
+    if "aug_period" in states.columns:
+        return states
+
+    if "period" not in states.columns:
+        return states
+
+    # Expand period rows into aug_period rows using the mapping.
+    if aug_periods_to_periods is not None:
+        mapping_df = pd.DataFrame(
+            list(aug_periods_to_periods.items()),
+            columns=["aug_period", "period"],
+        )
+        states = states.merge(mapping_df, on="period", how="left")
+        states = states.drop(columns=["period"])
+    else:
+        states = states.rename(columns={"period": "aug_period"})
+
+    return states
+
+
 def _prepare_data_for_one_plot_fixed_quantile_2d(
     states_data: pd.DataFrame,
     state_ranges: dict[str, pd.DataFrame],
@@ -550,21 +758,32 @@ def _prepare_data_for_one_plot_average_2d(
     input_min = state_ranges[input_factor].loc[aug_period]["minimum"]
     input_max = state_ranges[input_factor].loc[aug_period]["maximum"]
 
-    to_concat = []
-    for _, draw in draws.iterrows():
-        input_data = pd.DataFrame()
-        input_data[input_factor] = np.linspace(input_min, input_max, n_points)
-        for col, val in draw.items():
-            input_data[col] = val
-        input_arr = jnp.array(input_data[list(all_factors)].to_numpy())
-        # convert from jax to numpy array
-        output_arr = np.array(transition_function(transition_params, input_arr))
-        draw_data = pd.DataFrame()
-        draw_data[f"input_{input_factor}"] = input_data[input_factor]
-        draw_data[f"output_{output_factor}"] = np.array(output_arr)
-        to_concat.append(draw_data)
+    input_grid = np.linspace(input_min, input_max, n_points)
+    draws_arr = draws.to_numpy()  # (n_draws, n_sampled_factors)
 
-    return pd.concat(to_concat).groupby(f"input_{input_factor}").mean().reset_index()
+    # Build (n_draws * n_points, n_factors) array with broadcasting
+    tiled_input = np.tile(input_grid, n_draws)
+    repeated_draws = np.repeat(draws_arr, n_points, axis=0)
+
+    full_arr = np.empty((n_draws * n_points, len(all_factors)))
+    for i, factor in enumerate(all_factors):
+        if factor == input_factor:
+            full_arr[:, i] = tiled_input
+        else:
+            col_idx = sampled_factors.index(factor)
+            full_arr[:, i] = repeated_draws[:, col_idx]
+
+    output_arr = np.array(
+        transition_function(transition_params, jnp.array(full_arr)),
+    )
+    output_mean = output_arr.reshape(n_draws, n_points).mean(axis=0)
+
+    return pd.DataFrame(
+        {
+            f"input_{input_factor}": input_grid,
+            f"output_{output_factor}": output_mean,
+        }
+    )
 
 
 def _process_factor_mapping_trans(
diff --git a/tests/conftest.py b/tests/conftest.py
index 81b003e2..89d6f081 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1 +1,45 @@
 """Shared test fixtures and helpers."""
+
+from dataclasses import replace
+from pathlib import Path
+
+import pandas as pd
+import pytest
+
+from skillmodels.config import TEST_DATA_DIR
+from skillmodels.test_data.model2 import MODEL2
+
+REGRESSION_VAULT = Path(__file__).parent / "regression_vault"
+
+
+@pytest.fixture
+def model2():
+    """Return MODEL2 test model specification."""
+    return MODEL2
+
+
+@pytest.fixture
+def model2_data():
+    """Load model2 simulated data with (caseid, period) index."""
+    data = pd.read_stata(TEST_DATA_DIR / "model2_simulated_data.dta")
+    return data.set_index(["caseid", "period"])
+
+
+@pytest.fixture
+def model2_params():
+    """Load regression vault params for one_stage_anchoring."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    return params.set_index(["category", "period", "name1", "name2"])
+
+
+@pytest.fixture
+def model2_with_endogenous():
+    """Model2 with fac3 set as endogenous factor."""
+    fac3 = MODEL2.factors["fac3"]
+    new_fac3 = replace(fac3, is_endogenous=True)
+    new_factors = dict(MODEL2.factors) | {"fac3": new_fac3}
+    return MODEL2._replace(
+        factors=new_factors,
+        stagemap=None,
+        anchoring=None,
+    )
diff --git a/tests/test_check_model.py b/tests/test_check_model.py
new file mode 100644
index 00000000..7f1a36bf
--- /dev/null
+++ b/tests/test_check_model.py
@@ -0,0 +1,148 @@
+"""Tests for check_model validation functions."""
+
+from types import SimpleNamespace
+
+from skillmodels.check_model import (
+    _check_anchoring,
+    _check_loadings_are_not_normalized_to_zero,
+    _check_measurements,
+    _check_normalized_variables_are_present,
+    check_stagemap,
+)
+from skillmodels.model_spec import FactorSpec, ModelSpec, Normalizations
+
+
+def test_invalid_stagemap_length() -> None:
+    result = check_stagemap(
+        stagemap=(0, 0),
+        stages=(0,),
+        n_periods=5,
+        is_augmented=False,
+    )
+    assert any("length" in msg.lower() or "n_periods" in msg.lower() for msg in result)
+
+
+def test_invalid_anchoring_non_bool() -> None:
+    anchoring = SimpleNamespace(
+        anchoring="yes",  # not bool
+        outcomes={},
+        free_controls=False,
+        free_constant=False,
+        free_loadings=False,
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("bool" in msg for msg in result)
+
+
+def test_invalid_anchoring_non_mapping_outcomes() -> None:
+    anchoring = SimpleNamespace(
+        anchoring=True,
+        outcomes="not_a_mapping",
+        free_controls=False,
+        free_constant=False,
+        free_loadings=False,
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("Mapping" in msg for msg in result)
+
+
+def test_invalid_anchoring_outcome_type() -> None:
+    anchoring = SimpleNamespace(
+        anchoring=True,
+        outcomes={"f1": [1, 2, 3]},  # list is not str/int/tuple
+        free_controls=False,
+        free_constant=False,
+        free_loadings=False,
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("variable" in msg.lower() for msg in result)
+
+
+def test_invalid_anchoring_free_controls_type() -> None:
+    anchoring = SimpleNamespace(
+        anchoring=True,
+        outcomes={},
+        free_controls="yes",  # not bool
+        free_constant=False,
+        free_loadings=False,
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("free_controls" in msg for msg in result)
+
+
+def test_invalid_measurements_not_tuples() -> None:
+    spec = ModelSpec(
+        factors={
+            "f1": FactorSpec(
+                measurements=(["y1", "y2"],),  # ty: ignore[invalid-argument-type]
+            ),
+        },
+    )
+    result = _check_measurements(model_spec=spec, factors=("f1",))
+    assert any("tuples" in msg for msg in result)
+
+
+def test_invalid_measurement_type() -> None:
+    spec = ModelSpec(
+        factors={
+            "f1": FactorSpec(
+                measurements=((["nested_list"],),),  # ty: ignore[invalid-argument-type]
+            ),
+        },
+    )
+    result = _check_measurements(model_spec=spec, factors=("f1",))
+    assert any(
+        "column names" in msg.lower() or "tuples" in msg.lower() for msg in result
+    )
+
+
+def test_normalized_variable_not_in_measurements() -> None:
+    spec = ModelSpec(
+        factors={
+            "f1": FactorSpec(
+                measurements=(("y1", "y2"),),
+                normalizations=Normalizations(
+                    loadings=({"y99": 1},),
+                    intercepts=({},),
+                ),
+            ),
+        },
+    )
+    result = _check_normalized_variables_are_present(
+        list_of_normdicts=[{"y99": 1}],
+        model_spec=spec,
+        factor="f1",
+    )
+    assert any("y99" in msg for msg in result)
+
+
+def test_invalid_anchoring_free_constant_type() -> None:
+    anchoring = SimpleNamespace(
+        anchoring=True,
+        outcomes={},
+        free_controls=False,
+        free_constant="yes",
+        free_loadings=False,
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("free_constant" in msg for msg in result)
+
+
+def test_invalid_anchoring_free_loadings_type() -> None:
+    anchoring = SimpleNamespace(
+        anchoring=True,
+        outcomes={},
+        free_controls=False,
+        free_constant=False,
+        free_loadings="yes",
+    )
+    result = _check_anchoring(anchoring)  # ty: ignore[invalid-argument-type]
+    assert any("free_loadings" in msg for msg in result)
+
+
+def test_loading_normalized_to_zero() -> None:
+    result = _check_loadings_are_not_normalized_to_zero(
+        list_of_normdicts=[{"y1": 0}],
+        factor="f1",
+    )
+    assert any("0" in msg for msg in result)
diff --git a/tests/test_constraints.py b/tests/test_constraints.py
index e8fbaca4..6bb52c47 100644
--- a/tests/test_constraints.py
+++ b/tests/test_constraints.py
@@ -1,3 +1,5 @@
+"""Tests for constraints."""
+
 from types import MappingProxyType
 
 import numpy as np
@@ -15,6 +17,8 @@
     _get_stage_constraints,
     _get_transition_constraints,
     add_bounds,
+    constraints_dicts_to_om,
+    get_constraints_dicts,
 )
 from skillmodels.process_model import process_model
 from skillmodels.test_data.simplest_augmented_model import SIMPLEST_AUGMENTED_MODEL
@@ -40,11 +44,6 @@ def test_add_bounds() -> None:
     assert_frame_equal(calculated, expected)
 
 
-# ======================================================================================
-# constraints due to normalizations of loadings and variances
-# ======================================================================================
-
-
 def test_normalization_constraints() -> None:
     norm = {
         "fac1": Normalizations(
@@ -92,11 +91,6 @@ def test_normalization_constraints() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints for mixture weights
-# ======================================================================================
-
-
 def test_mixture_weight_constraints_mixture() -> None:
     calculated = _get_mixture_weights_constraints(n_mixtures=2)
     for c in calculated:
@@ -113,11 +107,6 @@ def test_mixture_weight_constraints_normal() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints for development stages
-# ======================================================================================
-
-
 def test_stage_constraints() -> None:
     stages = (0,)
     stagemap = (0, 0, 0)
@@ -167,11 +156,6 @@ def test_stage_constraints_with_endogenous_factors() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints for constant factors
-# ======================================================================================
-
-
 def test_constant_factor_constraints() -> None:
     labels = Labels(
         latent_factors=("fac1", "fac2"),
@@ -199,11 +183,6 @@ def test_constant_factor_constraints() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints that ensure the ordering of initial states (needed for identification)
-# ======================================================================================
-
-
 def test_initial_mean_constraints() -> None:
     nmixtures = 3
     factors = ("fac1", "fac2", "fac3")
@@ -221,11 +200,6 @@ def test_initial_mean_constraints() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints on transition parameters
-# ======================================================================================
-
-
 def test_trans_coeff_constraints() -> None:
     labels = Labels(
         latent_factors=("fac1", "fac2", "fac3"),
@@ -267,11 +241,6 @@ def test_trans_coeff_constraints() -> None:
     assert_list_equal_except_for_order(calculated, expected)
 
 
-# ======================================================================================
-# constraints on anchoring parameters
-# ======================================================================================
-
-
 @pytest.fixture
 def anch_uinfo():
     ind_tups = [
@@ -419,6 +388,89 @@ def simplest_augmented_model():
     return process_model(SIMPLEST_AUGMENTED_MODEL)
 
 
+def test_get_constraints_dicts_with_endogenous_factors(
+    simplest_augmented_model,
+) -> None:
+    constraints = get_constraints_dicts(
+        update_info=simplest_augmented_model.update_info,
+        labels=simplest_augmented_model.labels,
+        dimensions=simplest_augmented_model.dimensions,
+        anchoring_info=simplest_augmented_model.anchoring,
+        normalizations=simplest_augmented_model.normalizations,
+        endogenous_factors_info=simplest_augmented_model.endogenous_factors_info,
+    )
+    # Should contain augmented-period constraints
+    assert any(c.get("value") == 1e-08 for c in constraints)
+
+
+def test_constraints_dicts_to_om_type_dispatch(simplest_augmented_model) -> None:
+    constraints = get_constraints_dicts(
+        update_info=simplest_augmented_model.update_info,
+        labels=simplest_augmented_model.labels,
+        dimensions=simplest_augmented_model.dimensions,
+        anchoring_info=simplest_augmented_model.anchoring,
+        normalizations=simplest_augmented_model.normalizations,
+        endogenous_factors_info=simplest_augmented_model.endogenous_factors_info,
+    )
+    om_constraints = constraints_dicts_to_om(constraints)
+    assert len(om_constraints) > 0
+
+
+def test_constraints_dicts_to_om_equality_type() -> None:
+    dicts = [
+        {
+            "loc": ("loadings", 0, "y1", "f1"),
+            "type": "equality",
+            "id": 0,
+            "description": "test",
+        },
+    ]
+    result = constraints_dicts_to_om(dicts)
+    assert len(result) == 1
+
+
+def test_constraints_dicts_to_om_probability_type() -> None:
+    dicts = [
+        {
+            "loc": "mixture_weights",
+            "type": "probability",
+            "id": 0,
+            "description": "test",
+        },
+    ]
+    result = constraints_dicts_to_om(dicts)
+    assert len(result) == 1
+
+
+def test_constraints_dicts_to_om_increasing_type() -> None:
+    dicts = [
+        {
+            "loc": [
+                ("initial_states", 0, "m0", "f1"),
+                ("initial_states", 0, "m1", "f1"),
+            ],
+            "type": "increasing",
+            "id": 0,
+            "description": "test",
+        },
+    ]
+    result = constraints_dicts_to_om(dicts)
+    assert len(result) == 1
+
+
+def test_constraints_dicts_to_om_unknown_type_raises() -> None:
+    dicts = [
+        {
+            "loc": ("loadings", 0, "y1", "f1"),
+            "type": "unknown_type",
+            "id": 0,
+            "description": "test",
+        },
+    ]
+    with pytest.raises(TypeError, match="unknown_type"):
+        constraints_dicts_to_om(dicts)
+
+
 def test_get_constraints_for_augmented_periods(simplest_augmented_model) -> None:
     calculated = _get_constraints_for_augmented_periods(
         labels=simplest_augmented_model.labels,
diff --git a/tests/test_correlation_heatmap.py b/tests/test_correlation_heatmap.py
index 02811287..114dc9bc 100644
--- a/tests/test_correlation_heatmap.py
+++ b/tests/test_correlation_heatmap.py
@@ -1,7 +1,12 @@
+"""Tests for correlation heatmap."""
+
+from pathlib import Path
 from types import MappingProxyType, SimpleNamespace
 
 import numpy as np
 import pandas as pd
+import plotly.graph_objects as go
+import pytest
 from pandas.testing import assert_frame_equal as afe
 
 from skillmodels.correlation_heatmap import (
@@ -11,9 +16,15 @@
     _get_quasi_factor_scores_data_for_multiple_periods,
     _get_quasi_factor_scores_data_for_single_period,
     _process_factors,
+    get_measurements_corr,
+    get_quasi_scores_corr,
+    get_scores_corr,
+    plot_correlation_heatmap,
 )
 from skillmodels.types import Labels
 
+REGRESSION_VAULT = Path(__file__).parent / "regression_vault"
+
 
 def test_get_measurement_data_with_single_period() -> None:
     period = 1
@@ -327,3 +338,123 @@ def test_get_mask_full_square_matrix() -> None:
     expected = corr.to_numpy().astype(bool)
     result = _get_mask(corr, show_upper_triangle=show_upper, show_diagonal=show_diag)
     np.testing.assert_array_equal(result, expected)
+
+
+def _synthetic_corr():
+    """Return a synthetic 3x3 correlation DataFrame."""
+    data = np.array([[1.0, 0.5, 0.3], [0.5, 1.0, 0.7], [0.3, 0.7, 1.0]])
+    return pd.DataFrame(data, columns=["a", "b", "c"], index=["a", "b", "c"])
+
+
+def test_plot_correlation_heatmap_basic() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(corr)
+    assert isinstance(fig, go.Figure)
+    assert len(fig.data) > 0
+
+
+def test_plot_correlation_heatmap_no_diagonal() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(corr, show_diagonal=False)
+    assert isinstance(fig, go.Figure)
+
+
+def test_plot_correlation_heatmap_no_upper_triangle() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(corr, show_upper_triangle=False)
+    assert isinstance(fig, go.Figure)
+
+
+def test_plot_correlation_heatmap_annotations() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(corr, annotate=True)
+    assert isinstance(fig, go.Figure)
+    assert fig.layout.annotations is not None
+    assert len(fig.layout.annotations) > 0
+
+
+def test_plot_correlation_heatmap_custom_kwargs() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(
+        corr,
+        heatmap_kwargs={"colorscale": "Viridis"},
+        layout_kwargs={"title": "My Heatmap"},
+    )
+    assert fig.layout.title.text == "My Heatmap"
+
+
+def test_plot_correlation_heatmap_trim() -> None:
+    corr = _synthetic_corr()
+    fig = plot_correlation_heatmap(
+        corr, trim_heatmap=True, show_upper_triangle=False, show_diagonal=False
+    )
+    assert isinstance(fig, go.Figure)
+
+
+@pytest.mark.integration
+def test_get_measurements_corr(model2, model2_data) -> None:
+    result = get_measurements_corr(
+        data=model2_data, model_spec=model2, factors=None, periods=None
+    )
+    assert isinstance(result, pd.DataFrame)
+    assert result.shape[0] == result.shape[1]  # square
+
+
+@pytest.mark.integration
+def test_get_measurements_corr_single_period(model2, model2_data) -> None:
+    result = get_measurements_corr(
+        data=model2_data, model_spec=model2, factors=None, periods=0
+    )
+    assert isinstance(result, pd.DataFrame)
+    assert result.shape[0] == result.shape[1]
+
+
+@pytest.mark.integration
+def test_get_quasi_scores_corr(model2, model2_data) -> None:
+    result = get_quasi_scores_corr(
+        data=model2_data, model_spec=model2, factors=None, periods=None
+    )
+    assert isinstance(result, pd.DataFrame)
+
+
+@pytest.mark.integration
+def test_get_quasi_scores_corr_single_period(model2, model2_data) -> None:
+    result = get_quasi_scores_corr(
+        data=model2_data, model_spec=model2, factors=None, periods=0
+    )
+    assert isinstance(result, pd.DataFrame)
+    assert result.shape[0] == result.shape[1]
+
+
+@pytest.fixture
+def vault_params_for_scores():
+    """Load vault params with aug_period index level for get_scores_corr."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.rename(columns={"period": "aug_period"})
+    return params.set_index(["category", "aug_period", "name1", "name2"])
+
+
+@pytest.mark.integration
+def test_get_scores_corr(model2, model2_data, vault_params_for_scores) -> None:
+    result = get_scores_corr(
+        data=model2_data,
+        params=vault_params_for_scores,
+        model_spec=model2,
+        factors=None,
+        periods=None,
+    )
+    assert isinstance(result, pd.DataFrame)
+
+
+@pytest.mark.integration
+def test_get_scores_corr_single_period(
+    model2, model2_data, vault_params_for_scores
+) -> None:
+    result = get_scores_corr(
+        data=model2_data,
+        params=vault_params_for_scores,
+        model_spec=model2,
+        factors=None,
+        periods=0,
+    )
+    assert isinstance(result, pd.DataFrame)
diff --git a/tests/test_decorators.py b/tests/test_decorators.py
index fcfcc762..44dd7645 100644
--- a/tests/test_decorators.py
+++ b/tests/test_decorators.py
@@ -1,4 +1,7 @@
+"""Tests for decorators."""
+
 import jax.numpy as jnp
+import pytest
 
 from skillmodels.decorators import extract_params, jax_array_output, register_params
 
@@ -85,3 +88,8 @@ def f() -> str:
     g = register_params(f, params=["a", "b", "c"])
     assert g.__registered_params__ == ["a", "b", "c"]  # ty: ignore[unresolved-attribute]
     assert g() == "bla"
+
+
+def test_extract_params_no_key_no_names_raises() -> None:
+    with pytest.raises(ValueError, match="cannot both be None"):
+        extract_params(key=None, names=None)(lambda **kw: kw)
diff --git a/tests/test_diagnostic_plots.py b/tests/test_diagnostic_plots.py
new file mode 100644
index 00000000..c823aa39
--- /dev/null
+++ b/tests/test_diagnostic_plots.py
@@ -0,0 +1,131 @@
+"""Tests for diagnostic_plots module."""
+
+from pathlib import Path
+
+import pandas as pd
+import plotly.graph_objects as go
+import pytest
+
+from skillmodels.diagnostic_plots import (
+    plot_likelihood_contributions,
+    plot_residual_boxplots,
+)
+from skillmodels.maximization_inputs import get_maximization_inputs
+
+REGRESSION_VAULT = Path(__file__).parent / "regression_vault"
+
+
+@pytest.fixture
+def model2_diag_params(model2, model2_data):
+    """Prepare params that match the expected index for diagnostic plots."""
+    vault_params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    vault_params = vault_params.set_index(["category", "period", "name1", "name2"])
+    max_inputs = get_maximization_inputs(model_spec=model2, data=model2_data)
+    params = max_inputs["params_template"].copy()
+    # Fill in values from vault params where indices match
+    common_idx = params.index.intersection(vault_params.index)
+    params.loc[common_idx, "value"] = vault_params.loc[common_idx, "value"]
+    params["value"] = params["value"].fillna(0.0)
+    return params
+
+
+@pytest.mark.integration
+def test_plot_residual_boxplots_single_period(
+    model2, model2_data, model2_diag_params
+) -> None:
+    fig = plot_residual_boxplots(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=0,
+    )
+    assert isinstance(fig, go.Figure)
+    assert any(isinstance(t, go.Box) for t in fig.data)
+
+
+@pytest.mark.integration
+def test_plot_residual_boxplots_all_periods(
+    model2, model2_data, model2_diag_params
+) -> None:
+    result = plot_residual_boxplots(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=None,
+    )
+    assert isinstance(result, dict)
+    for fig in result.values():
+        assert isinstance(fig, go.Figure)
+
+
+@pytest.mark.integration
+def test_plot_residual_boxplots_no_reference_line(
+    model2, model2_data, model2_diag_params
+) -> None:
+    fig = plot_residual_boxplots(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=0,
+        show_reference_line=False,
+    )
+    assert isinstance(fig, go.Figure)
+
+
+@pytest.mark.integration
+def test_plot_residual_boxplots_layout_kwargs(
+    model2, model2_data, model2_diag_params
+) -> None:
+    fig = plot_residual_boxplots(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=0,
+        layout_kwargs={"title": "Custom Title"},
+    )
+    assert isinstance(fig, go.Figure)
+    assert fig.layout.title.text == "Custom Title"
+
+
+@pytest.mark.integration
+def test_plot_likelihood_contributions_single_period(
+    model2, model2_data, model2_diag_params
+) -> None:
+    fig = plot_likelihood_contributions(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=0,
+    )
+    assert isinstance(fig, go.Figure)
+    assert any(isinstance(t, go.Box) for t in fig.data)
+
+
+@pytest.mark.integration
+def test_plot_likelihood_contributions_all_periods(
+    model2, model2_data, model2_diag_params
+) -> None:
+    result = plot_likelihood_contributions(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=None,
+    )
+    assert isinstance(result, dict)
+    for fig in result.values():
+        assert isinstance(fig, go.Figure)
+
+
+@pytest.mark.integration
+def test_plot_likelihood_contributions_layout_kwargs(
+    model2, model2_data, model2_diag_params
+) -> None:
+    fig = plot_likelihood_contributions(
+        model_spec=model2,
+        data=model2_data,
+        params=model2_diag_params,
+        period=0,
+        layout_kwargs={"title": "Custom LL Title"},
+    )
+    assert isinstance(fig, go.Figure)
+    assert fig.layout.title.text == "Custom LL Title"
diff --git a/tests/test_filtered_states.py b/tests/test_filtered_states.py
index d3268196..787e9764 100644
--- a/tests/test_filtered_states.py
+++ b/tests/test_filtered_states.py
@@ -1,3 +1,5 @@
+"""Tests for filtered states."""
+
 from pathlib import Path
 
 import numpy as np
diff --git a/tests/test_kalman_filters.py b/tests/test_kalman_filters.py
index 26b578bc..eae8a12b 100644
--- a/tests/test_kalman_filters.py
+++ b/tests/test_kalman_filters.py
@@ -1,3 +1,5 @@
+"""Tests for Kalman filters."""
+
 from itertools import product
 
 import jax
@@ -19,10 +21,6 @@
 
 jax.config.update("jax_enable_x64", True)
 
-# ======================================================================================
-# Test Kalman Update with random state and cov againts filterpy
-# ======================================================================================
-
 SEEDS = range(20)
 UPDATE_FUNCS = [kalman_update, kalman_update_debug]
 
@@ -82,11 +80,6 @@ def test_kalman_update(seed, update_func) -> None:
     aaae(calculated_covs, expected_covs)
 
 
-# ======================================================================================
-# Test Kalman Update with missings
-# ======================================================================================
-
-
 @pytest.mark.parametrize("update_func", UPDATE_FUNCS)
 def test_kalman_update_with_missing(update_func) -> None:
     """State, cov and weights should not change, log likelihood should be zero."""
@@ -129,11 +122,6 @@ def test_kalman_update_with_missing(update_func) -> None:
     assert calc_weights.shape == weights.shape
 
 
-# ======================================================================================
-# test generation of sigma points
-# ======================================================================================
-
-
 @pytest.mark.parametrize("seed", SEEDS)
 def test_sigma_points(seed: int) -> None:
     rng = np.random.default_rng(seed)
@@ -153,11 +141,6 @@ def test_sigma_points(seed: int) -> None:
     aaae(calculated.reshape(expected.shape), expected)
 
 
-# ======================================================================================
-# Test sigma weights and scaling factor
-# ======================================================================================
-
-
 @pytest.mark.parametrize("seed", SEEDS)
 def test_sigma_scaling_factor_and_weights(seed) -> None:
     rng = np.random.default_rng(seed)
@@ -173,11 +156,6 @@ def test_sigma_scaling_factor_and_weights(seed) -> None:
     assert calc_scaling == np.sqrt(dim + kappa)
 
 
-# ======================================================================================
-# test transformation of sigma points
-# ======================================================================================
-
-
 def test_transformation_of_sigma_points() -> None:
     sp = jnp.arange(10).reshape(1, 1, 5, 2) + 1
 
@@ -205,14 +183,6 @@ def f(params, states):
     aaae(calculated, expected)
 
 
-# ======================================================================================
-# test special case against linear predict from filterpy
-# - anchoring scaling factors are 1
-# - anchoring constants are 0
-# - linear transition functions
-# ======================================================================================
-
-
 @pytest.mark.parametrize("seed", SEEDS)
 def test_predict_against_linear_filterpy(seed) -> None:
     rng = np.random.default_rng(seed)
@@ -262,11 +232,6 @@ def transition_function(params, states):
     aaae(calc_chols[0, 0].T @ calc_chols[0, 0], expected_cov)
 
 
-# ======================================================================================
-# Helper function to generate inputs and convert them between filterpy and skillmodels
-# ======================================================================================
-
-
 def _random_state_and_covariance(rng, dim=None):
     if dim is None:
         dim = rng.integers(low=1, high=10)
@@ -301,3 +266,40 @@ def _convert_predict_inputs_from_filterpy_to_skillmodels(state, cov):
     sm_state = jnp.array(state).reshape(1, 1, n_fac)
     sm_chol = jnp.array(scipy.linalg.cholesky(cov)).reshape(1, 1, n_fac, n_fac)
     return sm_state, sm_chol
+
+
+def test_sigma_points_multiple_mixtures() -> None:
+    """Sigma points should work with n_mixtures >= 2."""
+    n_obs = 2
+    n_mixtures = 2
+    n_states = 3
+    n_observed = 2
+    n_sigma = 2 * n_states + 1
+
+    rng = np.random.default_rng(42)
+    states = jnp.array(rng.standard_normal((n_obs, n_mixtures, n_states)))
+    upper_chols = jnp.array(
+        np.tile(np.eye(n_states), (n_obs, n_mixtures, 1, 1)),
+    )
+    observed_factors = jnp.array(rng.standard_normal((n_obs, n_observed)))
+    scaling_factor = float(jnp.sqrt(n_states + 2))
+
+    result = _calculate_sigma_points(
+        states=states,
+        upper_chols=upper_chols,
+        scaling_factor=scaling_factor,
+        observed_factors=observed_factors,
+    )
+
+    # Check output shape
+    assert result.shape == (n_obs, n_mixtures, n_sigma, n_states + n_observed)
+
+    # Observed columns should be constant across the sigma dimension
+    for obs in range(n_obs):
+        for mix in range(n_mixtures):
+            observed_slice = result[obs, mix, :, n_states:]
+            expected = jnp.broadcast_to(
+                observed_factors[obs],
+                (n_sigma, n_observed),
+            )
+            aaae(observed_slice, expected)
diff --git a/tests/test_likelihood_regression.py b/tests/test_likelihood_regression.py
index a6ccb176..cfe7a621 100644
--- a/tests/test_likelihood_regression.py
+++ b/tests/test_likelihood_regression.py
@@ -1,3 +1,5 @@
+"""Tests for likelihood regression."""
+
 import json
 from dataclasses import replace
 from itertools import product
diff --git a/tests/test_maximization_inputs.py b/tests/test_maximization_inputs.py
index 1f2dd6fa..eb807d48 100644
--- a/tests/test_maximization_inputs.py
+++ b/tests/test_maximization_inputs.py
@@ -2,8 +2,10 @@
 
 import jax.numpy as jnp
 import numpy as np
+import pandas as pd
+import pytest
 
-from skillmodels.maximization_inputs import _to_numpy
+from skillmodels.maximization_inputs import _get_jnp_params_vec, _to_numpy
 
 
 def test_to_numpy_with_dict() -> None:
@@ -24,3 +26,33 @@ def test_to_numpy_one_float() -> None:
     """Test _to_numpy with single float input."""
     calculated = _to_numpy(3.5)
     assert isinstance(calculated, float)
+
+
+def test_get_jnp_params_vec_missing_entries_raises() -> None:
+    target_index = pd.MultiIndex.from_tuples(
+        [("a", 0, "x", "y"), ("b", 0, "x", "y")],
+        names=["category", "period", "name1", "name2"],
+    )
+    # Params has only one of the two entries
+    params = pd.DataFrame(
+        {"value": [1.0]},
+        index=target_index[:1],
+    )
+    with pytest.raises(ValueError, match="missing entries"):
+        _get_jnp_params_vec(params, target_index)
+
+
+def test_get_jnp_params_vec_additional_entries_raises() -> None:
+    target_index = pd.MultiIndex.from_tuples(
+        [("a", 0, "x", "y")],
+        names=["category", "period", "name1", "name2"],
+    )
+    params = pd.DataFrame(
+        {"value": [1.0, 2.0]},
+        index=pd.MultiIndex.from_tuples(
+            [("a", 0, "x", "y"), ("extra", 0, "x", "y")],
+            names=["category", "period", "name1", "name2"],
+        ),
+    )
+    with pytest.raises(ValueError, match="additional entries"):
+        _get_jnp_params_vec(params, target_index)
diff --git a/tests/test_model_spec.py b/tests/test_model_spec.py
new file mode 100644
index 00000000..dda3958d
--- /dev/null
+++ b/tests/test_model_spec.py
@@ -0,0 +1,139 @@
+"""Tests for model_spec module."""
+
+import pytest
+
+from skillmodels.model_spec import AnchoringSpec, FactorSpec, ModelSpec, Normalizations
+from skillmodels.types import EstimationOptions
+
+
+def _minimal_dict():
+    """Return a minimal model dict for from_dict tests."""
+    return {
+        "factors": {
+            "f1": {
+                "measurements": [["y1", "y2"], ["y1", "y2"]],
+                "transition_function": "linear",
+            },
+        },
+    }
+
+
+def test_from_dict_minimal() -> None:
+    d = _minimal_dict()
+    spec = ModelSpec.from_dict(d)
+    assert "f1" in spec.factors
+    assert spec.factors["f1"].measurements == (("y1", "y2"), ("y1", "y2"))
+    assert spec.factors["f1"].transition_function == "linear"
+    assert spec.anchoring is None
+    assert spec.estimation_options is None
+    assert spec.controls == ()
+
+
+def test_from_dict_with_normalizations() -> None:
+    d = _minimal_dict()
+    d["factors"]["f1"]["normalizations"] = {
+        "loadings": [{"y1": 1}, {}],
+        "intercepts": [{}, {}],
+    }
+    spec = ModelSpec.from_dict(d)
+    norms = spec.factors["f1"].normalizations
+    assert norms is not None
+    assert len(norms.loadings) == 2
+
+
+def test_from_dict_normalizations_default_intercepts() -> None:
+    d = _minimal_dict()
+    d["factors"]["f1"]["normalizations"] = {
+        "loadings": [{"y1": 1}, {}],
+    }
+    spec = ModelSpec.from_dict(d)
+    norms = spec.factors["f1"].normalizations
+    assert norms is not None
+    assert len(norms.intercepts) == 2
+    # Default intercepts are empty dicts
+    assert dict(norms.intercepts[0]) == {}
+
+
+def test_from_dict_with_anchoring() -> None:
+    d = _minimal_dict()
+    d["anchoring"] = {"outcomes": {"f1": "Q1"}, "free_controls": True}
+    spec = ModelSpec.from_dict(d)
+    assert spec.anchoring is not None
+    assert dict(spec.anchoring.outcomes) == {"f1": "Q1"}
+    assert spec.anchoring.free_controls is True
+
+
+def test_from_dict_with_estimation_options() -> None:
+    d = _minimal_dict()
+    d["estimation_options"] = {"n_mixtures": 2, "robust_bounds": False}
+    spec = ModelSpec.from_dict(d)
+    assert spec.estimation_options is not None
+    assert spec.estimation_options.n_mixtures == 2
+    assert spec.estimation_options.robust_bounds is False
+
+
+def test_from_dict_with_stagemap() -> None:
+    d = _minimal_dict()
+    d["stagemap"] = [0]
+    spec = ModelSpec.from_dict(d)
+    assert spec.stagemap == (0,)
+
+
+def test_with_added_factor(model2) -> None:
+    new_factor = FactorSpec(
+        measurements=(("z1", "z2"),) * 8,
+        transition_function="linear",
+    )
+    result = model2.with_added_factor("fac4", new_factor)
+    assert len(result.factors) == 4
+    assert "fac4" in result.factors
+
+
+def test_with_added_observed_factors(model2) -> None:
+    result = model2.with_added_observed_factors("obs1", "obs2")
+    assert result.observed_factors == ("obs1", "obs2")
+
+
+def test_with_estimation_options(model2) -> None:
+    opts = EstimationOptions(n_mixtures=3)
+    result = model2.with_estimation_options(opts)
+    assert result.estimation_options is not None
+    assert result.estimation_options.n_mixtures == 3
+
+
+def test_with_anchoring(model2) -> None:
+    anch = AnchoringSpec(outcomes={"fac2": "Q2"})
+    result = model2.with_anchoring(anch)
+    assert result.anchoring is not None
+    assert dict(result.anchoring.outcomes) == {"fac2": "Q2"}
+
+
+def test_with_controls(model2) -> None:
+    result = model2.with_controls(("x1", "x2"))
+    assert result.controls == ("x1", "x2")
+
+
+def test_with_stagemap(model2) -> None:
+    result = model2.with_stagemap((0, 1, 2, 3, 4, 5, 6))
+    assert result.stagemap == (0, 1, 2, 3, 4, 5, 6)
+
+
+def test_with_transition_functions_valid(model2) -> None:
+    funcs = {"fac1": "linear", "fac2": "translog", "fac3": "constant"}
+    result = model2.with_transition_functions(funcs)
+    assert result.factors["fac1"].transition_function == "linear"
+    assert result.factors["fac2"].transition_function == "translog"
+
+
+def test_factor_spec_with_normalizations() -> None:
+    fspec = FactorSpec(measurements=(("y1", "y2"),))
+    norms = Normalizations(loadings=({"y1": 1},), intercepts=({},))
+    result = fspec.with_normalizations(norms)
+    assert result.normalizations is not None
+    assert dict(result.normalizations.loadings[0]) == {"y1": 1}
+
+
+def test_with_transition_functions_mismatched_keys_raises(model2) -> None:
+    funcs = {"fac1": "linear", "wrong": "translog"}
+    with pytest.raises(ValueError, match="do not match"):
+        model2.with_transition_functions(funcs)
diff --git a/tests/test_params_index.py b/tests/test_params_index.py
index 338d1e26..9a3fc6c0 100644
--- a/tests/test_params_index.py
+++ b/tests/test_params_index.py
@@ -1,3 +1,5 @@
+"""Tests for params index."""
+
 from types import MappingProxyType
 
 import pandas as pd
@@ -37,9 +39,10 @@ def test_params_index_with_model2(model2_inputs) -> None:
     calculated = get_params_index(**model2_inputs)
     expected = pd.read_csv(
         TEST_DATA_DIR / "model2_correct_params_index.csv",
-        index_col=["category", "period", "name1", "name2"],
+        index_col=["category", "aug_period", "name1", "name2"],
     ).index
 
+    assert calculated.names == list(expected.names)
     assert calculated.equals(expected)
 
 
diff --git a/tests/test_parse_params.py b/tests/test_parse_params.py
index 7ee94891..07272511 100644
--- a/tests/test_parse_params.py
+++ b/tests/test_parse_params.py
@@ -25,7 +25,7 @@
 def parsed_parameters():
     p_index = pd.read_csv(
         TEST_DATA_DIR / "model2_correct_params_index.csv",
-        index_col=["category", "period", "name1", "name2"],
+        index_col=["category", "aug_period", "name1", "name2"],
     ).index
 
     processed = process_model(MODEL2)
diff --git a/tests/test_process_data.py b/tests/test_process_data.py
index d988956c..ec2fce30 100644
--- a/tests/test_process_data.py
+++ b/tests/test_process_data.py
@@ -1,3 +1,5 @@
+"""Tests for process data."""
+
 import io
 import textwrap
 from types import MappingProxyType
@@ -168,6 +170,40 @@ def test_generate_observed_factor_array() -> None:
     aae(calculated, expected)
 
 
+def test_augment_data_unbalanced_raises(simplest_augmented) -> None:
+    processed_model = process_model(simplest_augmented["model"])
+    pre_processed = pre_process_data(
+        simplest_augmented["data_input"], processed_model.labels.periods
+    )
+    pre_processed["constant"] = 1
+    # Drop a row to make it unbalanced
+    pre_processed = pre_processed.iloc[:-1]
+    with pytest.raises(ValueError, match="not balanced"):
+        _augment_data_for_endogenous_factors(
+            df=pre_processed,
+            labels=processed_model.labels,
+            update_info=processed_model.update_info,
+        )
+
+
+def test_augment_data_wrong_periods_raises(simplest_augmented) -> None:
+    processed_model = process_model(simplest_augmented["model"])
+    pre_processed = pre_process_data(
+        simplest_augmented["data_input"], processed_model.labels.periods
+    )
+    pre_processed["constant"] = 1
+    # Shift periods to create mismatch
+    df = pre_processed.reset_index()
+    df["period"] = df["period"] + 10
+    df = df.set_index(["id", "period"])
+    with pytest.raises(ValueError, match=r"[Pp]eriods"):
+        _augment_data_for_endogenous_factors(
+            df=df,
+            labels=processed_model.labels,
+            update_info=processed_model.update_info,
+        )
+
+
 def _read_csv_string(string, index_cols):
     string = textwrap.dedent(string)
     return pd.read_csv(io.StringIO(string), index_col=index_cols)
diff --git a/tests/test_process_debug_data.py b/tests/test_process_debug_data.py
new file mode 100644
index 00000000..4b69e118
--- /dev/null
+++ b/tests/test_process_debug_data.py
@@ -0,0 +1,59 @@
+"""Tests for process_debug_data module."""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from skillmodels.process_debug_data import (
+    _create_post_update_states,
+    _process_residuals,
+    create_state_ranges,
+)
+
+
+def test_create_state_ranges_invalid_quantile_raises() -> None:
+    states = pd.DataFrame({"fac1": [1.0, 2.0, 3.0, 4.0], "period": [0, 0, 1, 1]})
+    with pytest.raises(ValueError, match="quantile_cutoff"):
+        create_state_ranges(states, factors=["fac1"], quantile_cutoff=1.5)
+
+
+def test_process_residuals_ids_with_mixtures() -> None:
+    """ID column should repeat observation ids, not be sequential."""
+    n_obs = 3
+    n_mixtures = 2
+    residuals = [np.ones((n_obs, n_mixtures)) * i for i in range(2)]
+    update_info = pd.DataFrame(
+        index=pd.MultiIndex.from_tuples([(0, "m1"), (0, "m2")]),
+    )
+
+    result = _process_residuals(residuals=residuals, update_info=update_info)  # ty: ignore[invalid-argument-type]
+
+    # For each update, ids should be [0, 0, 1, 1, 2, 2] not [0, 1, 2, 3, 4, 5]
+    for _, group in result.groupby(["aug_period", "measurement"]):
+        expected_ids = np.repeat(np.arange(n_obs), n_mixtures)
+        np.testing.assert_array_equal(group["id"].to_numpy(), expected_ids)
+
+
+def test_create_post_update_states_ids_with_mixtures() -> None:
+    """ID column should repeat observation ids per mixture."""
+    n_obs = 3
+    n_mixtures = 2
+    n_states = 2
+    factors = ("fac1", "fac2")
+
+    filtered_states = np.array(
+        [np.ones((n_obs, n_mixtures, n_states)) * i for i in range(2)]
+    )
+    update_info = pd.DataFrame(
+        index=pd.MultiIndex.from_tuples([(0, "m1"), (0, "m2")]),
+    )
+
+    result = _create_post_update_states(
+        filtered_states=filtered_states,  # ty: ignore[invalid-argument-type]
+        factors=factors,
+        update_info=update_info,
+    )
+
+    for _, group in result.groupby(["aug_period", "measurement"]):
+        expected_ids = np.repeat(np.arange(n_obs), n_mixtures)
+        np.testing.assert_array_equal(group["id"].to_numpy(), expected_ids)
diff --git a/tests/test_process_model.py b/tests/test_process_model.py
index 3c9df1c2..3b9a03e3 100644
--- a/tests/test_process_model.py
+++ b/tests/test_process_model.py
@@ -1,6 +1,9 @@
+"""Tests for process model."""
+
 import inspect
 from dataclasses import replace
 
+import numpy as np
 import pandas as pd
 import pytest
 from pandas.testing import assert_frame_equal
@@ -11,10 +14,6 @@
 from skillmodels.test_data.model2 import MODEL2
 from skillmodels.types import Normalizations, TransitionInfo
 
-# ======================================================================================
-# Integration test with model2 from the replication files of CHS2010
-# ======================================================================================
-
 
 @pytest.fixture
 def model2():
@@ -22,7 +21,7 @@ def model2():
 
 
 def test_has_endogenous_factors(model2) -> None:
-    assert process_model(model2).endogenous_factors_info.has_endogenous_factors == False
+    assert not process_model(model2).endogenous_factors_info.has_endogenous_factors
 
 
 def test_dimensions(model2) -> None:
@@ -50,7 +49,7 @@ def test_estimation_options(model2) -> None:
     res = process_model(model2).estimation_options
     assert res.sigma_points_scale == 2
     assert res.robust_bounds
-    assert res.bounds_distance == 0.001
+    assert np.isclose(res.bounds_distance, 0.001)
 
 
 def test_anchoring(model2) -> None:
@@ -118,11 +117,6 @@ def test_normalizations(model2) -> None:
     assert res == expected
 
 
-# ======================================================================================
-# Augment model2 with endogenous factors
-# ======================================================================================
-
-
 def _make_fac3_endogenous(model):
     """Return a new model with fac3 set as endogenous."""
     fac3 = model.factors["fac3"]
@@ -180,9 +174,7 @@ def model2_inv():
 
 
 def test_with_endog_has_endogenous_factors(model2_inv) -> None:
-    assert (
-        process_model(model2_inv).endogenous_factors_info.has_endogenous_factors == True
-    )
+    assert process_model(model2_inv).endogenous_factors_info.has_endogenous_factors
 
 
 def test_with_endog_dimensions(model2_inv) -> None:
@@ -213,7 +205,7 @@ def test_with_endog_estimation_options(model2_inv) -> None:
     res = process_model(model2_inv).estimation_options
     assert res.sigma_points_scale == 2
     assert res.robust_bounds
-    assert res.bounds_distance == 0.001
+    assert np.isclose(res.bounds_distance, 0.001)
 
 
 def test_with_endog_anchoring_is_empty(model2_inv) -> None:
@@ -315,11 +307,6 @@ def test_with_endog_normalizations(model2_inv) -> None:
     assert res == expected
 
 
-# ======================================================================================
-# Unit tests
-# ======================================================================================
-
-
 def _fspec(**kwargs) -> FactorSpec:
     """Create a minimal FactorSpec for unit tests."""
     return FactorSpec(measurements=((),), **kwargs)
@@ -327,12 +314,12 @@ def _fspec(**kwargs) -> FactorSpec:
 
 def test_model_has_endogenous_factors_not_specified() -> None:
     factors = {"a": _fspec()}
-    assert get_has_endogenous_factors(factors) == False
+    assert not get_has_endogenous_factors(factors)
 
 
 def test_get_has_endogenous_factors_wrong_constellation() -> None:
     factors = {"a": _fspec(is_endogenous=False, is_correction=True)}
-    with pytest.raises(ValueError):
+    with pytest.raises(ValueError, match="is_endogenous"):
         get_has_endogenous_factors(factors)
 
 
@@ -341,7 +328,7 @@ def test_get_has_endogenous_factors_indeed() -> None:
         "a": _fspec(is_endogenous=True, is_correction=False),
         "b": _fspec(is_endogenous=False, is_correction=False),
     }
-    assert get_has_endogenous_factors(factors) == True
+    assert get_has_endogenous_factors(factors)
 
 
 def test_get_has_endogenous_factors_and_correction() -> None:
@@ -350,4 +337,4 @@ def test_get_has_endogenous_factors_and_correction() -> None:
         "b": _fspec(is_endogenous=False, is_correction=False),
         "c": _fspec(is_endogenous=True, is_correction=True),
     }
-    assert get_has_endogenous_factors(factors) == True
+    assert get_has_endogenous_factors(factors)
diff --git a/tests/test_simulate_data.py b/tests/test_simulate_data.py
index c2b742ab..782367e7 100644
--- a/tests/test_simulate_data.py
+++ b/tests/test_simulate_data.py
@@ -1,6 +1,5 @@
 """Tests for functions in simulate_data module."""
 
-from dataclasses import replace
 from pathlib import Path
 
 import numpy as np
@@ -8,36 +7,32 @@
 import pytest
 from numpy.testing import assert_array_almost_equal as aaae
 
-from skillmodels.config import TEST_DATA_DIR
+from skillmodels.model_spec import (
+    EstimationOptions,
+    FactorSpec,
+    ModelSpec,
+    Normalizations,
+)
+from skillmodels.params_index import get_params_index
 from skillmodels.process_model import process_model
 from skillmodels.simulate_data import (
     _collapse_aug_periods_to_periods,
+    _get_shock,
     measurements_from_states,
     simulate_dataset,
+    simulate_policy_effect,
 )
 from skillmodels.test_data.model2 import MODEL2
 
 REGRESSION_VAULT = Path(__file__).parent / "regression_vault"
 
 
-@pytest.fixture
-def model2():
-    return MODEL2
-
-
-@pytest.fixture
-def model2_data():
-    data = pd.read_stata(TEST_DATA_DIR / "model2_simulated_data.dta")
-    return data.set_index(["caseid", "period"])
-
-
 def test_simulate_dataset(model2, model2_data) -> None:
-    model = model2
     params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
     params = params.set_index(["category", "period", "name1", "name2"])
 
     calculated = simulate_dataset(
-        model_spec=model,
+        model_spec=model2,
         params=params,
         data=model2_data,
     )
@@ -65,19 +60,6 @@ def test_measurements_from_factors() -> None:
     )
 
 
-@pytest.fixture
-def model2_with_endogenous():
-    """Model2 with fac3 set as endogenous factor."""
-    fac3 = MODEL2.factors["fac3"]
-    new_fac3 = replace(fac3, is_endogenous=True)
-    new_factors = dict(MODEL2.factors) | {"fac3": new_fac3}
-    return MODEL2._replace(
-        factors=new_factors,
-        stagemap=None,
-        anchoring=None,
-    )
-
-
 def test_collapse_aug_periods_to_periods_with_endogenous_factors(
     model2_with_endogenous,
 ) -> None:
@@ -98,7 +80,7 @@ def test_collapse_aug_periods_to_periods_with_endogenous_factors(
     records = []
     for aug_p in range(n_aug_periods):
         for obs_id in range(n_obs):
-            record = {"id": obs_id, "aug_period": aug_p}
+            record: dict[str, int | float] = {"id": obs_id, "aug_period": aug_p}
             for fac in factors:
                 record[fac] = rng.standard_normal()
             records.append(record)
@@ -128,3 +110,160 @@ def test_collapse_aug_periods_to_periods_with_endogenous_factors(
 
     # Should have all observations for each period
     assert len(result) == n_obs * expected_n_periods
+
+
+def test_simulate_dataset_no_data_no_nobs_raises(model2) -> None:
+    """Both data=None and n_obs=None should raise ValueError."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+    with pytest.raises(ValueError, match="Either `data` or `n_obs`"):
+        simulate_dataset(model_spec=model2, params=params, data=None, n_obs=None)
+
+
+def test_simulate_dataset_observed_factors_without_data_raises() -> None:
+    """Model with observed factors requires data."""
+    model = MODEL2.with_added_observed_factors("obs1")
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+    with pytest.raises(ValueError, match="observed factors"):
+        simulate_dataset(model_spec=model, params=params, data=None, n_obs=100)
+
+
+def test_simulate_dataset_controls_without_data_raises(model2) -> None:
+    """Model with non-constant controls requires data."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+    with pytest.raises(ValueError, match="controls"):
+        simulate_dataset(model_spec=model2, params=params, data=None, n_obs=100)
+
+
+def test_simulate_policy_effect(model2, model2_data) -> None:
+    """Deterministic policy should produce non-zero diffs."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+
+    policies = [
+        {
+            "aug_period": 0,
+            "factor": "fac1",
+            "effect_size": 0.5,
+            "standard_deviation": 0,
+        },
+    ]
+    result = simulate_policy_effect(
+        model_spec=model2,
+        params=params,
+        data=model2_data,
+        policies=policies,
+        seed=42,
+    )
+    assert isinstance(result, pd.DataFrame)
+    assert "fac1" in result.columns
+    # The policy effect should show up as non-zero differences
+    assert not np.allclose(result["fac1"].to_numpy(), 0)
+
+
+def test_simulate_policy_effect_unanchored(model2, model2_data) -> None:
+    """Unanchored states should also work."""
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+
+    policies = [
+        {
+            "aug_period": 0,
+            "factor": "fac1",
+            "effect_size": 0.5,
+            "standard_deviation": 0,
+        },
+    ]
+    result = simulate_policy_effect(
+        model_spec=model2,
+        params=params,
+        data=model2_data,
+        policies=policies,
+        seed=42,
+        use_anchored_states=False,
+    )
+    assert isinstance(result, pd.DataFrame)
+    assert not np.allclose(result["fac1"].to_numpy(), 0)
+
+
+def test_get_shock_deterministic() -> None:
+    """sd=0 should produce array of all equal values."""
+    rng = np.random.default_rng(42)
+    result = _get_shock(rng, mean=1.5, sd=0, size=10)
+    aaae(result, np.full(10, 1.5))
+
+
+def test_get_shock_stochastic() -> None:
+    """sd>0 should produce array of correct shape."""
+    rng = np.random.default_rng(42)
+    result = _get_shock(rng, mean=0.0, sd=1.0, size=20)
+    assert result.shape == (20,)
+
+
+def test_simulate_dataset_nobs_mismatch_warns(model2, model2_data) -> None:
+    params = pd.read_csv(REGRESSION_VAULT / "one_stage_anchoring.csv")
+    params = params.set_index(["category", "period", "name1", "name2"])
+    with pytest.warns(UserWarning, match="n_obs"):
+        simulate_dataset(model_spec=model2, params=params, data=model2_data, n_obs=999)
+
+
+def test_get_shock_negative_sd_raises() -> None:
+    """sd<0 should raise ValueError."""
+    rng = np.random.default_rng(42)
+    with pytest.raises(ValueError, match="negative standard deviation"):
+        _get_shock(rng, mean=0.0, sd=-1.0, size=10)
+
+
+def test_simulate_dataset_no_data_with_nobs() -> None:
+    """Simulate with data=None and n_obs should work for controls-free model."""
+    model_no_controls = ModelSpec(
+        factors={
+            "fac1": FactorSpec(
+                measurements=(("y1", "y2", "y3"),) * 3,
+                normalizations=Normalizations(
+                    loadings=({"y1": 1},) * 3,
+                    intercepts=({},) * 3,
+                ),
+                transition_function="linear",
+            ),
+            "fac2": FactorSpec(
+                measurements=(("y4", "y5", "y6"),) * 3,
+                normalizations=Normalizations(
+                    loadings=({"y4": 1},) * 3,
+                    intercepts=({},) * 3,
+                ),
+                transition_function="linear",
+            ),
+        },
+        estimation_options=EstimationOptions(
+            robust_bounds=True,
+            bounds_distance=0.001,
+            n_mixtures=1,
+        ),
+    )
+
+    processed = process_model(model_no_controls)
+    p_index = get_params_index(
+        update_info=processed.update_info,
+        labels=processed.labels,
+        dimensions=processed.dimensions,
+        transition_info=processed.transition_info,
+        endogenous_factors_info=processed.endogenous_factors_info,
+    )
+    params = pd.DataFrame({"value": np.ones(len(p_index)) * 0.5}, index=p_index)
+
+    result = simulate_dataset(
+        model_spec=model_no_controls,
+        params=params,
+        n_obs=50,
+        data=None,
+        seed=42,
+    )
+
+    assert "unanchored_states" in result
+    states = result["unanchored_states"]["states"]
+    assert len(states) > 0
+    assert "fac1" in states.columns
+    assert "fac2" in states.columns
diff --git a/tests/test_transition_functions.py b/tests/test_transition_functions.py
index 0a84e7e7..562dfbf7 100644
--- a/tests/test_transition_functions.py
+++ b/tests/test_transition_functions.py
@@ -1,13 +1,31 @@
+"""Tests for transition functions."""
+
 import jax
 import jax.numpy as jnp
+import numpy as np
+import pytest
 from numpy.testing import assert_array_almost_equal as aaae
 
 from skillmodels.transition_functions import (
     constant,
+    constraints_log_ces,
+    identity_constraints_linear,
+    identity_constraints_linear_and_squares,
+    identity_constraints_log_ces,
+    identity_constraints_log_ces_general,
+    identity_constraints_robust_translog,
+    identity_constraints_translog,
     linear,
+    linear_and_squares,
     log_ces,
     log_ces_general,
+    params_constant,
+    params_linear,
+    params_linear_and_squares,
+    params_log_ces,
     params_log_ces_general,
+    params_robust_translog,
+    params_translog,
     robust_translog,
     translog,
 )
@@ -147,3 +165,113 @@ def test_param_names_log_ces_general() -> None:
     expected = ["a", "b", "sigma_a", "sigma_b", "tfp"]
     calculated = params_log_ces_general(factors)
     assert calculated == expected
+
+
+def test_params_linear() -> None:
+    factors = ("a", "b", "c")
+    result = params_linear(factors)
+    assert result == ["a", "b", "c", "constant"]
+
+
+def test_params_translog() -> None:
+    factors = ("a", "b", "c")
+    result = params_translog(factors)
+    # 3 linear + 3 squares + 3 interactions + 1 constant = 10
+    assert len(result) == 10
+    assert result[:3] == ["a", "b", "c"]
+    assert result[3:6] == ["a ** 2", "b ** 2", "c ** 2"]
+    assert result[6:9] == ["a * b", "a * c", "b * c"]
+    assert result[9] == "constant"
+
+
+def test_params_robust_translog() -> None:
+    factors = ("a", "b", "c")
+    assert params_robust_translog(factors) == params_translog(factors)
+
+
+def test_params_linear_and_squares() -> None:
+    factors = ("a", "b", "c")
+    result = params_linear_and_squares(factors)
+    # 3 linear + 3 squares + 1 constant = 7
+    assert len(result) == 7
+    assert result[:3] == ["a", "b", "c"]
+    assert result[3:6] == ["a ** 2", "b ** 2", "c ** 2"]
+    assert result[6] == "constant"
+
+
+def test_params_constant() -> None:
+    assert params_constant(("a", "b", "c")) == []
+
+
+def test_params_log_ces() -> None:
+    factors = ("a", "b", "c")
+    result = params_log_ces(factors)
+    assert result == ["a", "b", "c", "phi"]
+
+
+def test_linear_and_squares() -> None:
+    states = jnp.array([1.0, 2.0, 3.0])
+    # 3 linear + 3 square + 1 constant = 7
+    params = jnp.array([0.1, 0.2, 0.3, 0.01, 0.02, 0.03, 0.5])
+    expected = 1.4 + 0.36 + 0.5
+    aaae(linear_and_squares(states, params), expected)
+
+
+def test_identity_constraints_linear() -> None:
+    all_factors = ("a", "b", "c")
+    result = identity_constraints_linear("a", 0, all_factors)
+    assert len(result) == 4  # 3 factors + constant
+    # "a" regressor should be fixed at 1.0
+    assert np.isclose(result[0]["value"], 1.0)
+    assert result[0]["loc"] == ("transition", 0, "a", "a")
+    # others should be 0.0
+    assert np.isclose(result[1]["value"], 0.0)
+    assert np.isclose(result[3]["value"], 0.0)  # constant
+
+
+def test_identity_constraints_translog() -> None:
+    all_factors = ("a", "b", "c")
+    result = identity_constraints_translog("a", 0, all_factors)
+    # Should have one constraint per translog param
+    assert len(result) == len(params_translog(all_factors))
+    # First constraint for "a" linear should be 1.0
+    assert np.isclose(result[0]["value"], 1.0)
+    # All others should be 0.0
+    for c in result[1:]:
+        assert np.isclose(c["value"], 0.0)
+
+
+def test_identity_constraints_robust_translog() -> None:
+    all_factors = ("a", "b")
+    result_robust = identity_constraints_robust_translog("a", 0, all_factors)
+    result_translog = identity_constraints_translog("a", 0, all_factors)
+    assert result_robust == result_translog
+
+
+def test_identity_constraints_linear_and_squares() -> None:
+    all_factors = ("a", "b", "c")
+    result = identity_constraints_linear_and_squares("a", 0, all_factors)
+    assert len(result) == len(params_linear_and_squares(all_factors))
+    assert np.isclose(result[0]["value"], 1.0)  # "a" linear
+    for c in result[1:]:
+        assert np.isclose(c["value"], 0.0)
+
+
+def test_identity_constraints_log_ces_raises() -> None:
+    with pytest.raises(NotImplementedError, match=r"^$"):
+        identity_constraints_log_ces(("a", "b"), 0, ("a", "b"))
+
+
+def test_identity_constraints_log_ces_general_raises() -> None:
+    with pytest.raises(NotImplementedError, match=r"^$"):
+        identity_constraints_log_ces_general(("a", "b"), 0, ("a", "b"))
+
+
+def test_constraints_log_ces() -> None:
+    result = constraints_log_ces("fac1", ("a", "b", "c"), 0)
+    assert result["type"] == "probability"
+    assert len(result["loc"]) == 3  # gamma constraints for a, b, c (not phi)
+    for loc in result["loc"]:
+        assert loc[0] == "transition"
+        assert loc[1] == 0
+        assert loc[2] == "fac1"
diff --git a/tests/test_types.py b/tests/test_types.py
new file mode 100644
index 00000000..4f03b868
--- /dev/null
+++ b/tests/test_types.py
@@ -0,0 +1,37 @@
+"""Tests for types module."""
+
+import pickle
+from types import MappingProxyType
+
+import pytest
+
+from skillmodels.types import FactorInfo, _make_immutable
+
+
+def test_make_immutable_list_to_tuple() -> None:
+    assert _make_immutable([1, 2]) == (1, 2)
+
+
+def test_make_immutable_set_to_frozenset() -> None:
+    assert _make_immutable({1, 2}) == frozenset({1, 2})
+
+
+def test_mapping_proxy_pickle_roundtrip() -> None:
+    mp = MappingProxyType({"a": 1, "b": [2, 3]})
+    result = pickle.loads(pickle.dumps(mp))  # noqa: S301
+    assert dict(result) == {"a": 1, "b": (2, 3)}
+
+
+def test_factor_info_from_flags_all_false() -> None:
+    info = FactorInfo.from_flags(is_endogenous=False, is_correction=False)
+    assert info.is_state
+
+
+def test_factor_info_from_flags_correction() -> None:
+    info = FactorInfo.from_flags(is_endogenous=True, is_correction=True)
+    assert info.is_correction
+
+
+def test_factor_info_from_flags_both_true_raises() -> None:
+    with pytest.raises(ValueError, match=r"correction.*endogenous"):
+        FactorInfo.from_flags(is_endogenous=False, is_correction=True)
diff --git a/tests/test_utilities.py b/tests/test_utilities.py
index 5540f9c6..66e05632 100644
--- a/tests/test_utilities.py
+++ b/tests/test_utilities.py
@@ -14,6 +14,7 @@
 from skillmodels.process_model import process_model
 from skillmodels.test_data.model2 import MODEL2
 from skillmodels.utilities import (
+    _extend_params,
     _get_params_index,
     extract_factors,
     reduce_n_periods,
@@ -184,4 +185,108 @@ def test_extend_params_via_switch_to_translog(model2) -> None:
 
     assert_index_equal(added_index, expected_added_index)
 
-    assert extended_params.loc[added_index, "value"].unique()[0] == 0.05
+    assert np.isclose(extended_params.loc[added_index, "value"].unique()[0], 0.05)
+
+
+def test_update_parameter_values_single_df() -> None:
+    """Pass a single DataFrame instead of a list."""
+    params = pd.DataFrame()
+    params["value"] = np.arange(5, dtype=np.int64)
+
+    other = pd.DataFrame([[7], [8]], columns=["value"], index=[1, 4])
+
+    expected = pd.DataFrame()
+    expected["value"] = [0, 7, 2, 3, 8]
+
+    calculated = update_parameter_values(params, other)
+    assert_frame_equal(calculated, expected)
+
+
+def test_remove_measurements_with_params(model2) -> None:
+    """Remove measurements with params and verify tuple return."""
+    model = reduce_n_periods(model2, 2)
+    assert isinstance(model, ModelSpec)
+    full_index = _get_params_index(model)
+    params = pd.DataFrame(columns=["value"], index=full_index)
+    params["value"] = 0.1
+
+    result = remove_measurements("y5", model, params)
+    assert not isinstance(result, ModelSpec)
+    reduced_model, reduced_params = result
+    # y5 should not appear in any factor's measurements
+    for fspec in reduced_model.factors.values():
+        for period_meas in fspec.measurements:
+            assert "y5" not in period_meas
+    assert isinstance(reduced_params, pd.DataFrame)
+
+
+def test_remove_controls_with_params(model2) -> None:
+    """Remove controls with params and verify tuple return."""
+    model = reduce_n_periods(model2, 2)
+    assert isinstance(model, ModelSpec)
+    full_index = _get_params_index(model)
+    params = pd.DataFrame(columns=["value"], index=full_index)
+    params["value"] = 0.1
+
+    result = remove_controls("x1", model, params)
+    assert not isinstance(result, ModelSpec)
+    reduced_model, reduced_params = result
+    assert reduced_model.controls == ()
+    assert isinstance(reduced_params, pd.DataFrame)
+
+
+def test_remove_measurements_warns_on_normalized(model2) -> None:
+    with pytest.warns(UserWarning, match="normalized"):
+        remove_measurements("y1", model2)
+
+
+def test_reduce_n_periods_with_params(model2) -> None:
+    model = reduce_n_periods(model2, 2)
+    assert isinstance(model, ModelSpec)
+    full_index = _get_params_index(model)
+    params = pd.DataFrame(columns=["value"], index=full_index)
+    params["value"] = 0.1
+
+    result = reduce_n_periods(model, 1, params)
+    assert not isinstance(result, ModelSpec)
+    _, reduced_params = result
+    assert len(reduced_params) < len(params)
+
+
+def test_extend_params_with_bounds(model2) -> None:
+    model = reduce_n_periods(model2, 2)
+    assert isinstance(model, ModelSpec)
+    full_index = _get_params_index(model)
+    params = pd.DataFrame(columns=["value"], index=full_index)
+    params["value"] = 0.1
+    params["lower_bound"] = -1.0
+    params["upper_bound"] = 1.0
+
+    translog = switch_linear_to_translog(model)
+    assert isinstance(translog, ModelSpec)
+    result = _extend_params(params=params, model_spec=translog, fill_value=0.05)
+    assert "lower_bound" in result.columns
+    assert "upper_bound" in result.columns
+    # New entries should have default bounds
+    new_rows = result.index.difference(full_index)
+    assert (result.loc[new_rows, "lower_bound"] == -np.inf).all()
+    assert (result.loc[new_rows, "upper_bound"] == np.inf).all()
+
+
+def test_switch_translog_to_linear_with_params(model2) -> None:
+    """Switch translog to linear with params."""
+    # First switch to translog, then back to linear with params
+    with_translog = switch_linear_to_translog(model2)
+    assert isinstance(with_translog, ModelSpec)
+
+    model = reduce_n_periods(with_translog, 2)
+    assert isinstance(model, ModelSpec)
+    full_index = _get_params_index(model)
+    params = pd.DataFrame(columns=["value"], index=full_index)
+    params["value"] = 0.1
+
+    result = switch_translog_to_linear(model, params)
+    assert not isinstance(result, ModelSpec)
+    reduced_model, reduced_params = result
+    assert reduced_model.factors["fac2"].transition_function == "linear"
+    assert isinstance(reduced_params, pd.DataFrame)
diff --git a/tests/test_utils_plotting.py b/tests/test_utils_plotting.py
new file mode 100644
index 00000000..728677a2
--- /dev/null
+++ b/tests/test_utils_plotting.py
@@ -0,0 +1,55 @@
+"""Tests for utils_plotting module."""
+
+import numpy as np
+
+from skillmodels.utils_plotting import get_layout_kwargs, get_make_subplot_kwargs
+
+
+def test_get_layout_kwargs_defaults() -> None:
+    result = get_layout_kwargs()
+    assert result["template"] == "simple_white"
+    assert result["showlegend"] is False
+
+
+def test_get_layout_kwargs_override() -> None:
+    result = get_layout_kwargs(layout_kwargs={"title": "X"})
+    assert result["title"] == "X"
+
+
+def test_get_layout_kwargs_with_legend_kwargs() -> None:
+    result = get_layout_kwargs(legend_kwargs={"x": 0.5})
+    assert np.isclose(result["legend"]["x"], 0.5)
+
+
+def test_get_layout_kwargs_with_title_kwargs() -> None:
+    result = get_layout_kwargs(title_kwargs={"text": "Hello"})
+    assert result["title"] == {"text": "Hello"}
+
+
+def test_get_layout_kwargs_with_rows_and_cols() -> None:
+    result = get_layout_kwargs(rows=["a", "b"], columns=["c"])
+    assert result["height"] == 600
+    assert result["width"] == 300
+
+
+def test_get_make_subplot_kwargs_defaults() -> None:
+    result = get_make_subplot_kwargs(
+        sharex=False,
+        sharey=False,
+        column_order=["a", "b"],
+        row_order=["r1"],
+        make_subplot_kwargs=None,
+    )
+    assert result["rows"] == 1
+    assert result["cols"] == 2
+
+
+def test_get_make_subplot_kwargs_override() -> None:
+    result = get_make_subplot_kwargs(
+        sharex=True,
+        sharey=True,
+        column_order=["a"],
+        row_order=["r1"],
+        make_subplot_kwargs={"print_grid": True},
+    )
+    assert result["print_grid"] is True
diff --git a/tests/test_variance_decomposition.py b/tests/test_variance_decomposition.py
new file mode 100644
index 00000000..acfa924e
--- /dev/null
+++ b/tests/test_variance_decomposition.py
@@ -0,0 +1,112 @@
+"""Tests for variance decomposition."""
+
+import pandas as pd
+import pytest
+from numpy.testing import assert_array_almost_equal as aaae
+
+from skillmodels.variance_decomposition import (
+    _compute_variance_decomposition,
+    summarize_measurement_reliability,
+)
+
+
+@pytest.fixture
+def setup_variance_decomposition():
+    """Create test data for variance decomposition."""
+    # Filtered states with known variances
+    # fac1: variance = 0.0025 (values 0.1, 0.1, 0.1, 0.2)
+    # fac2: variance = 0 (all 0.1)
+    # fac3: variance = 0.01 (values 0.2, 0.2, 0.2, 0.4)
+    filtered_states = pd.DataFrame(
+        {
+            "fac1": [0.1, 0.1, 0.1, 0.2],
+            "fac2": [0.1, 0.1, 0.1, 0.1],
+            "fac3": [0.2, 0.2, 0.2, 0.4],
+            "period": [0, 0, 0, 0],
+            "id": [0, 1, 2, 3],
+        }
+    )
+
+    # Parameters: loadings and measurement SDs
+    # y1: loading=1 on fac1, meas_sd=0.05
+    # y2: loading=0.1 on fac2, meas_sd=1.1
+    # y3: loading=2 on fac3, meas_sd=0.1
+
+    loadings_data = []
+    for aug_period in [0]:
+        for meas, factor, loading in [
+            ("y1", "fac1", 1.0),
+            ("y1", "fac2", 0.0),
+            ("y1", "fac3", 0.0),
+            ("y2", "fac1", 0.0),
+            ("y2", "fac2", 0.1),
+            ("y2", "fac3", 0.0),
+            ("y3", "fac1", 0.0),
+            ("y3", "fac2", 0.0),
+            ("y3", "fac3", 2.0),
+        ]:
+            loadings_data.append((aug_period, meas, factor, loading))
+
+    loadings_df = pd.DataFrame(
+        loadings_data, columns=["aug_period", "name1", "name2", "value"]
+    )
+    loadings_df = loadings_df.set_index(["aug_period", "name1", "name2"])
+
+    meas_sds_data = [(0, "y1", "-", 0.05), (0, "y2", "-", 1.1), (0, "y3", "-", 0.1)]
+    meas_sds_df = pd.DataFrame(
+        meas_sds_data, columns=["aug_period", "name1", "name2", "value"]
+    )
+    meas_sds_df = meas_sds_df.set_index(["aug_period", "name1", "name2"])
+
+    params = pd.concat([loadings_df, meas_sds_df], keys=["loadings", "meas_sds"])
+
+    return {
+        "filtered_states": filtered_states,
+        "params": params,
+        "aug_periods_to_periods": {0: 0},
+    }
+
+
+@pytest.fixture
+def expected_variance_decomposition():
+    """Expected results for variance decomposition test.
+
+    Expected signal/noise fractions:
+    - y1: 50% signal, 50% noise (loading 1, var 0.0025, sd 0.05)
+    - y2: 0% signal, 100% noise (loading 0.1, var 0, sd 1.1)
+    - y3: 80% signal, 20% noise (loading 2, var 0.01, sd 0.1)
+    """
+    index = pd.MultiIndex.from_tuples(
+        [(0, "y1", "fac1"), (0, "y2", "fac2"), (0, "y3", "fac3")],
+        names=["period", "measurement", "factor"],
+    )
+    return pd.DataFrame(
+        {
+            "loading": [1.0, 0.1, 2.0],
+            "factor_variance": [0.0025, 0.0, 0.01],
+            "meas_sd": [0.05, 1.1, 0.1],
+            "fraction_signal": [0.5, 0.0, 0.8],
+            "fraction_noise": [0.5, 1.0, 0.2],
+            "signal_to_noise_ratio": [1.0, 0.0, 4.0],
+        },
+        index=index,
+    )
+
+
+def test_compute_variance_decomposition(
+    setup_variance_decomposition, expected_variance_decomposition
+):
+    """Test that variance decomposition computes correct fractions."""
+    result = _compute_variance_decomposition(**setup_variance_decomposition)
+    aaae(result.values, expected_variance_decomposition.values)
+
+
+def test_summarize_measurement_reliability(expected_variance_decomposition):
+    """Test summary statistics for measurement reliability."""
+    summary = summarize_measurement_reliability(expected_variance_decomposition)
+
+    assert "y3" in summary.index  # Highest signal
+    assert "y2" in summary.index  # Lowest signal
+    assert summary.loc["y3", "mean_signal"] == pytest.approx(0.8)
+    assert summary.loc["y2", "mean_signal"] == pytest.approx(0.0)
+    assert summary.loc["y1", "mean_signal"] == pytest.approx(0.5)
diff --git a/tests/test_visualize_factor_distributions.py b/tests/test_visualize_factor_distributions.py
index 74574fab..3baa03bb 100644
--- a/tests/test_visualize_factor_distributions.py
+++ b/tests/test_visualize_factor_distributions.py
@@ -1,3 +1,5 @@
+"""Tests for visualize factor distributions."""
+
 from pathlib import Path
 
 import pandas as pd
@@ -5,7 +7,6 @@
 from skillmodels.config import TEST_DATA_DIR
 from skillmodels.filtered_states import get_filtered_states
 from skillmodels.maximization_inputs import get_maximization_inputs
-from skillmodels.process_model import process_model
 from skillmodels.simulate_data import simulate_dataset
 from skillmodels.test_data.model2 import MODEL2
 from skillmodels.visualize_factor_distributions import (
@@ -108,20 +109,11 @@ def test_visualize_factor_distributions_with_period_indexed_states() -> None:
     max_inputs = get_maximization_inputs(model, data)
     params = params.loc[max_inputs["params_template"].index]
 
-    # Get filtered states and convert to (id, period) index without aug_period
+    # Get filtered states (already has period column) and set index
     filtered_states = get_filtered_states(model_spec=model, data=data, params=params)[
         "anchored_states"
     ]["states"]
-    processed = process_model(model)
-
-    # Add period column and drop aug_period
-    # (mimics task_filtered_states_and_measurements)
-    filtered_states["period"] = filtered_states["aug_period"].map(
-        processed.labels.aug_periods_to_periods
-    )
-    filtered_states = filtered_states.drop(columns=["aug_period"]).set_index(
-        ["id", "period"]
-    )
+    filtered_states = filtered_states.set_index(["id", "period"])
 
     kde = univariate_densities(
         data=data,
@@ -161,16 +153,10 @@ def test_visualize_factor_distributions_with_both_aug_period_and_period() -> Non
     max_inputs = get_maximization_inputs(model, data)
     params = params.loc[max_inputs["params_template"].index]
 
-    # Get filtered states and add period while keeping aug_period
+    # Get filtered states (already has period column) and set index
     filtered_states = get_filtered_states(model_spec=model, data=data, params=params)[
         "anchored_states"
     ]["states"]
-    processed = process_model(model)
-
-    # Add period column but keep aug_period (both are present)
-    filtered_states["period"] = filtered_states["aug_period"].map(
-        processed.labels.aug_periods_to_periods
-    )
     filtered_states = filtered_states.set_index(["id", "period"])
 
     kde = univariate_densities(
diff --git a/tests/test_visualize_transition_equations.py b/tests/test_visualize_transition_equations.py
index 3ce950fc..de630f7a 100644
--- a/tests/test_visualize_transition_equations.py
+++ b/tests/test_visualize_transition_equations.py
@@ -1,3 +1,5 @@
+"""Tests for visualize transition equations."""
+
 from pathlib import Path
 
 import pandas as pd